stim lfp response
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Mikkel Elle Lepperød
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registered: '2019-10-14T17:03:05'
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data:
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results: results.csv
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notebook: 10-calculate-stimulus-lfp-response.ipynb
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html: 10-calculate-stimulus-lfp-response.html
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notebook: 20_stimulus-lfp-response.ipynb
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html: 20_stimulus-lfp-response.html
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<div class="output_subarea output_stream output_stderr output_text">
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<pre>15:15:05 [I] klustakwik KlustaKwik2 version 0.2.6
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<pre>10:26:07 [I] klustakwik KlustaKwik2 version 0.2.6
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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-lfp-response'</span><span class="p">)</span>
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<span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">'figures'</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">'data'</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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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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<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>
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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="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="n">NFFT</span> <span class="o">=</span> <span class="mi">10000</span>
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<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>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">channel_groups</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">psd</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
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<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">channel_groups</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">channel_groups</span><span class="p">)):</span>
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<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>
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<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>
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<span class="n">action_group</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>
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<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>
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<span class="n">clean_lfp</span> <span class="o">=</span> <span class="n">select_and_clean</span><span class="p">(</span><span class="n">lfp</span><span class="p">)</span>
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<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>
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<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>
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<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>
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<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>
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<span class="n">mask</span> <span class="o">=</span> <span class="n">get_mask</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">lim</span><span class="p">)</span>
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<span class="n">signal</span> <span class="o">=</span> <span class="n">zscore</span><span class="p">(</span><span class="n">clean_lfp</span><span class="p">[</span><span class="n">mask</span><span class="p">,</span> <span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
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<span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="n">mlab</span><span class="o">.</span><span class="n">psd</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">Fs</span><span class="o">=</span><span class="n">lfp</span><span class="o">.</span><span class="n">sampling_rate</span><span class="o">.</span><span class="n">magnitude</span><span class="p">,</span> <span class="n">NFFT</span><span class="o">=</span><span class="n">NFFT</span><span class="p">)</span>
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<span class="n">psd</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">action_group</span><span class="p">:</span> <span class="n">p_xx</span><span class="p">})</span>
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<span class="n">freqs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">action_group</span><span class="p">:</span> <span class="n">freq</span><span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">psd</span><span class="p">)</span><span class="o">.</span><span class="n">to_feather</span><span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">'data'</span> <span class="o">/</span> <span class="s1">'psd.feather'</span><span class="p">)</span>
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<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>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
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<span class="ansi-red-fg">AttributeError</span> Traceback (most recent call last)
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<span class="ansi-green-fg"><ipython-input-18-7660e1d3307d></span> in <span class="ansi-cyan-fg"><module></span>
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<span class="ansi-green-fg">----> 1</span><span class="ansi-red-fg"> </span>psd<span class="ansi-blue-fg">.</span>to_feather<span class="ansi-blue-fg">(</span>output <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'psd.feather'</span><span class="ansi-blue-fg">)</span>
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<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>
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<span class="ansi-red-fg">AttributeError</span>: 'dict' object has no attribute 'to_feather'</pre>
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{
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|
||||
{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "81d917d31c0e43cf80ec3398d46b2c01",
|
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"model_id": "e733903bb8d7406ea6c05bf5af093bfc",
|
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|
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},
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|
|||
},
|
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"metadata": {},
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|
||||
},
|
||||
{
|
||||
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||||
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|
||||
"text": [
|
||||
"\n"
|
||||
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|
||||
}
|
||||
],
|
||||
"source": [
|
||||
|
|
@ -327,6 +334,77 @@
|
|||
" left_index=True, right_index=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
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||||
{
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"data": {
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"model_id": "0184251a265a40a2a1e9910405f05db8",
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"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(IntProgress(value=0, max=704), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
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||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"psd, freqs = {}, {}\n",
|
||||
"for i, row in tqdm(channel_groups.iterrows(), total=len(channel_groups)):\n",
|
||||
" action_id = row['action']\n",
|
||||
" channel_group = row['channel_group']\n",
|
||||
" action_group = f'{action_id}_{channel_group}'\n",
|
||||
" lfp = data_loader.lfp(action_id, channel_group)\n",
|
||||
" clean_lfp = select_and_clean(lfp)\n",
|
||||
" snls = signaltonoise(clean_lfp)\n",
|
||||
" best_channel = np.argmax(snls)\n",
|
||||
" snl = snls[best_channel]\n",
|
||||
" \n",
|
||||
" lim = get_lim(action_id)\n",
|
||||
" \n",
|
||||
" mask = get_mask(lfp, lim)\n",
|
||||
" signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
|
||||
" \n",
|
||||
" p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
|
||||
" psd.update({action_group: p_xx})\n",
|
||||
" freqs.update({action_group: freq})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'dict' object has no attribute 'to_feather'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-18-7660e1d3307d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpsd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'psd.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mfreqs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'freqs.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'to_feather'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"(psd).to_feather(output / 'data' / 'psd.feather')\n",
|
||||
"freqs.to_feather(output / 'data' / 'freqs.feather')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
|
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Reference in New Issue