auto commit actions/calculate-statistics
This commit is contained in:
Mikkel Elle Lepperød
parent
fcf3c9bf25
commit
7c18a9f762
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@ -13173,7 +13173,7 @@ div#notebook {
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<div class="output_subarea output_stream output_stderr output_text">
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<pre>08:27:22 [I] klustakwik KlustaKwik2 version 0.2.6
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<pre>17:02:20 [I] klustakwik KlustaKwik2 version 0.2.6
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</pre>
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</div>
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</div>
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@ -13192,7 +13192,7 @@ div#notebook {
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<span class="n">position_sampling_rate</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># for interpolation</span>
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<span class="n">position_low_pass_frequency</span> <span class="o">=</span> <span class="mi">6</span> <span class="c1"># for low pass filtering of position</span>
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<span class="n">box_size</span> <span class="o">=</span> <span class="mf">1.0</span>
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<span class="n">box_size</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]</span>
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<span class="n">bin_size</span> <span class="o">=</span> <span class="mf">0.02</span>
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<span class="n">smoothing_low</span> <span class="o">=</span> <span class="mf">0.03</span>
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<span class="n">smoothing_high</span> <span class="o">=</span> <span class="mf">0.06</span>
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@ -13264,39 +13264,57 @@ div#notebook {
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<th></th>
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<th>action</th>
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<th>channel_group</th>
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<th>max_depth_delta</th>
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<th>max_dissimilarity</th>
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<th>unit_id</th>
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<th>unit_name</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>1834-150319-3</td>
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<td>1834-010319-1</td>
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<td>0</td>
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<td>71</td>
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<td>100</td>
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<td>0.05</td>
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<td>8d8cecbe-e2e5-4020-9c94-9573ca55cdfc</td>
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<td>2</td>
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</tr>
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<tr>
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<th>1</th>
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<td>1834-150319-3</td>
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<td>1834-010319-1</td>
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<td>0</td>
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<td>75</td>
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<td>100</td>
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<td>0.05</td>
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<td>5b7fc3e8-b76d-4eed-a876-9ba184e508ac</td>
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<td>39</td>
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</tr>
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<tr>
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<th>2</th>
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<td>1834-120319-4</td>
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<td>0</td>
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<td>85</td>
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</tr>
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<tr>
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<th>3</th>
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<td>1834-120319-1</td>
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<td>1834-010319-3</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>1b42831d-5d71-4cb1-ba85-b5019b56ca2e</td>
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<td>1</td>
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</tr>
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<tr>
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<th>4</th>
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<td>1834-120319-2</td>
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<th>3</th>
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<td>1834-010319-3</td>
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<td>0</td>
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<td>39</td>
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<td>100</td>
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<td>0.05</td>
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<td>270fb3b3-3a7d-4060-bc1a-bc68d2ecab1a</td>
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<td>12</td>
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</tr>
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<tr>
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<th>4</th>
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<td>1834-010319-3</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>6da7e1db-2d4f-4bd7-b45c-a1855aaa2fec</td>
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<td>72</td>
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</tr>
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</tbody>
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</table>
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@ -13328,7 +13346,7 @@ div#notebook {
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="prompt input_prompt">In [9]:</div>
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<div class="prompt input_prompt">In [7]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">first_row</span> <span class="o">=</span> <span class="n">units</span><span class="p">[</span><span class="n">units</span><span class="p">[</span><span class="s1">'action'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'1849-060319-3'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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@ -13342,7 +13360,7 @@ div#notebook {
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="prompt input_prompt">In [10]:</div>
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<div class="prompt input_prompt">In [ ]:</div>
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<div class="inner_cell">
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<div class="input_area">
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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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@ -13464,56 +13482,6 @@ div#notebook {
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stderr output_text">
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<pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.
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warnings.warn("Instantaneous firing rate approximation contains "
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt output_prompt">Out[10]:</div>
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<div class="output_text output_subarea output_execute_result">
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<pre>average_rate 3.095328
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speed_score -0.063922
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out_field_mean_rate 1.837642
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in_field_mean_rate 5.122323
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max_field_mean_rate 8.882211
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max_rate 23.006163
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sparsity 0.468122
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selectivity 7.306812
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interspike_interval_cv 3.970863
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burst_event_ratio 0.397921
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bursty_spike_ratio 0.676486
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gridness -0.459487
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border_score 0.078474
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information_rate 0.965845
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head_mean_ang 5.788704
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head_mean_vec_len 0.043321
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spacing 0.624971
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orientation 22.067900
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dtype: float64</pre>
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</div>
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</div>
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</div>
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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@ -13529,56 +13497,6 @@ dtype: float64</pre>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="prompt"></div>
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<div id="a5b4d8e7-42ab-4477-9986-69ee9a0f9f55"></div>
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<div class="output_subarea output_widget_view ">
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<script type="text/javascript">
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var element = $('#a5b4d8e7-42ab-4477-9986-69ee9a0f9f55');
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</script>
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<script type="application/vnd.jupyter.widget-view+json">
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{"model_id": "837fde7fe486422bac67341ff512a4e1", "version_major": 2, "version_minor": 0}
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</script>
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</div>
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</div>
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<div class="output_subarea output_stream output_stderr output_text">
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<pre>/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2
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return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
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/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2
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return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
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/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply
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return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
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/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:56: RuntimeWarning: Mean of empty slice.
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/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
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ret = ret.dtype.type(ret / rcount)
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/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:57: RuntimeWarning: Mean of empty slice.
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/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:82: RuntimeWarning: invalid value encountered in long_scalars
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</pre>
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</div>
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@ -13617,7 +13535,7 @@ var element = $('#a5b4d8e7-42ab-4477-9986-69ee9a0f9f55');
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="prompt input_prompt">In [ ]:</div>
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<div class="prompt input_prompt">In [14]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">statistics_action</span> <span class="o">=</span> <span class="n">project</span><span class="o">.</span><span class="n">require_action</span><span class="p">(</span><span class="s2">"calculate-statistics"</span><span class="p">)</span>
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@ -13630,7 +13548,7 @@ var element = $('#a5b4d8e7-42ab-4477-9986-69ee9a0f9f55');
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="prompt input_prompt">In [ ]:</div>
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<div class="prompt input_prompt">In [15]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">statistics_action</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">"units"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"units.csv"</span>
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@ -13642,10 +13560,32 @@ var element = $('#a5b4d8e7-42ab-4477-9986-69ee9a0f9f55');
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_area">
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<div class="prompt output_prompt">Out[15]:</div>
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<div class="output_text output_subarea output_execute_result">
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<pre>['/media/storage/expipe/septum-mec/actions/calculate-statistics/data/results.csv',
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'/media/storage/expipe/septum-mec/actions/calculate-statistics/data/sessions.csv',
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'/media/storage/expipe/septum-mec/actions/calculate-statistics/data/units.csv']</pre>
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</div>
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</div>
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</div>
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="prompt input_prompt">In [ ]:</div>
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<div class="prompt input_prompt">In [16]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">septum_mec</span><span class="o">.</span><span class="n">analysis</span><span class="o">.</span><span class="n">registration</span><span class="o">.</span><span class="n">store_notebook</span><span class="p">(</span><span class="n">statistics_action</span><span class="p">,</span> <span class="s2">"10_calculate_spatial_statistics.ipynb"</span><span class="p">)</span>
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@ -19,7 +19,7 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"08:27:22 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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"17:02:20 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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]
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}
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],
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@ -62,7 +62,7 @@
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"position_sampling_rate = 100 # for interpolation\n",
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"position_low_pass_frequency = 6 # for low pass filtering of position\n",
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"\n",
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"box_size = 1.0\n",
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"box_size = [1.0, 1.0]\n",
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"bin_size = 0.02\n",
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"smoothing_low = 0.03\n",
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"smoothing_high = 0.06"
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@ -108,51 +108,76 @@
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" <th></th>\n",
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" <th>action</th>\n",
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" <th>channel_group</th>\n",
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" <th>max_depth_delta</th>\n",
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" <th>max_dissimilarity</th>\n",
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" <th>unit_id</th>\n",
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" <th>unit_name</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1834-150319-3</td>\n",
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" <td>1834-010319-1</td>\n",
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" <td>0</td>\n",
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" <td>71</td>\n",
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" <td>100</td>\n",
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" <td>0.05</td>\n",
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" <td>8d8cecbe-e2e5-4020-9c94-9573ca55cdfc</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1834-150319-3</td>\n",
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" <td>1834-010319-1</td>\n",
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" <td>0</td>\n",
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" <td>75</td>\n",
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" <td>100</td>\n",
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" <td>0.05</td>\n",
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" <td>5b7fc3e8-b76d-4eed-a876-9ba184e508ac</td>\n",
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" <td>39</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1834-120319-4</td>\n",
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" <td>0</td>\n",
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" <td>85</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1834-120319-1</td>\n",
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" <td>1834-010319-3</td>\n",
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" <td>0</td>\n",
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" <td>100</td>\n",
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" <td>0.05</td>\n",
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" <td>1b42831d-5d71-4cb1-ba85-b5019b56ca2e</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1834-120319-2</td>\n",
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" <th>3</th>\n",
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" <td>1834-010319-3</td>\n",
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" <td>0</td>\n",
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" <td>39</td>\n",
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" <td>100</td>\n",
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" <td>0.05</td>\n",
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" <td>270fb3b3-3a7d-4060-bc1a-bc68d2ecab1a</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1834-010319-3</td>\n",
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" <td>0</td>\n",
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" <td>100</td>\n",
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" <td>0.05</td>\n",
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" <td>6da7e1db-2d4f-4bd7-b45c-a1855aaa2fec</td>\n",
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" <td>72</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" action channel_group unit_name\n",
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"0 1834-150319-3 0 71\n",
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"1 1834-150319-3 0 75\n",
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"2 1834-120319-4 0 85\n",
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"3 1834-120319-1 0 1\n",
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"4 1834-120319-2 0 39"
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" action channel_group max_depth_delta max_dissimilarity \\\n",
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"0 1834-010319-1 0 100 0.05 \n",
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"1 1834-010319-1 0 100 0.05 \n",
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"2 1834-010319-3 0 100 0.05 \n",
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"3 1834-010319-3 0 100 0.05 \n",
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"4 1834-010319-3 0 100 0.05 \n",
|
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"\n",
|
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" unit_id unit_name \n",
|
||||
"0 8d8cecbe-e2e5-4020-9c94-9573ca55cdfc 2 \n",
|
||||
"1 5b7fc3e8-b76d-4eed-a876-9ba184e508ac 39 \n",
|
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"2 1b42831d-5d71-4cb1-ba85-b5019b56ca2e 1 \n",
|
||||
"3 270fb3b3-3a7d-4060-bc1a-bc68d2ecab1a 12 \n",
|
||||
"4 6da7e1db-2d4f-4bd7-b45c-a1855aaa2fec 72 "
|
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]
|
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},
|
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"execution_count": 5,
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|
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@ -181,7 +206,7 @@
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 9,
|
||||
"execution_count": 7,
|
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -191,48 +216,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
||||
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"average_rate 3.095328\n",
|
||||
"speed_score -0.063922\n",
|
||||
"out_field_mean_rate 1.837642\n",
|
||||
"in_field_mean_rate 5.122323\n",
|
||||
"max_field_mean_rate 8.882211\n",
|
||||
"max_rate 23.006163\n",
|
||||
"sparsity 0.468122\n",
|
||||
"selectivity 7.306812\n",
|
||||
"interspike_interval_cv 3.970863\n",
|
||||
"burst_event_ratio 0.397921\n",
|
||||
"bursty_spike_ratio 0.676486\n",
|
||||
"gridness -0.459487\n",
|
||||
"border_score 0.078474\n",
|
||||
"information_rate 0.965845\n",
|
||||
"head_mean_ang 5.788704\n",
|
||||
"head_mean_vec_len 0.043321\n",
|
||||
"spacing 0.624971\n",
|
||||
"orientation 22.067900\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def process(row):\n",
|
||||
" action_id = row['action']\n",
|
||||
|
|
@ -355,39 +343,7 @@
|
|||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "837fde7fe486422bac67341ff512a4e1",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(IntProgress(value=0, max=1281), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:56: RuntimeWarning: Mean of empty slice.\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
|
||||
" ret = ret.dtype.type(ret / rcount)\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:57: RuntimeWarning: Mean of empty slice.\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:82: RuntimeWarning: invalid value encountered in long_scalars\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results = units.merge(\n",
|
||||
" units.progress_apply(process, axis=1), \n",
|
||||
|
|
@ -423,7 +379,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -432,9 +388,22 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['/media/storage/expipe/septum-mec/actions/calculate-statistics/data/results.csv',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/calculate-statistics/data/sessions.csv',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/calculate-statistics/data/units.csv']"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"statistics_action.data[\"units\"] = \"units.csv\"\n",
|
||||
"statistics_action.data[\"results\"] = \"results.csv\"\n",
|
||||
|
|
@ -443,7 +412,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
|
|||
Loading…
Reference in New Issue