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Mikkel Elle Lepperød 2021-03-10 13:58:30 +01:00
parent 1652ab405e
commit 719518c73d
6 changed files with 2636 additions and 1349 deletions
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actions/calculate-statistics/data/10_calculate_spatial_statistics.html
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@ -13153,6 +13153,7 @@ div#notebook {
<span class="kn">import</span> <span class="nn">septum_mec</span> <span class="kn">import</span> <span class="nn">septum_mec</span>
<span class="kn">import</span> <span class="nn">scipy.ndimage.measurements</span> <span class="kn">import</span> <span class="nn">scipy.ndimage.measurements</span>
<span class="kn">from</span> <span class="nn">distutils.dir_util</span> <span class="k">import</span> <span class="n">copy_tree</span> <span class="kn">from</span> <span class="nn">distutils.dir_util</span> <span class="k">import</span> <span class="n">copy_tree</span>
<span class="kn">from</span> <span class="nn">spike_statistics.core</span> <span class="k">import</span> <span class="n">theta_mod_idx</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm_notebook</span> <span class="k">as</span> <span class="n">tqdm</span> <span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm_notebook</span> <span class="k">as</span> <span class="n">tqdm</span>
<span class="kn">from</span> <span class="nn">tqdm._tqdm_notebook</span> <span class="k">import</span> <span class="n">tqdm_notebook</span> <span class="kn">from</span> <span class="nn">tqdm._tqdm_notebook</span> <span class="k">import</span> <span class="n">tqdm_notebook</span>
@ -13173,7 +13174,9 @@ div#notebook {
<div class="output_subarea output_stream output_stderr output_text"> <div class="output_subarea output_stream output_stderr output_text">
<pre>17:02:17 [I] klustakwik KlustaKwik2 version 0.2.6 <pre>20:57:48 [I] klustakwik KlustaKwik2 version 0.2.6
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:25: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`
</pre> </pre>
</div> </div>
</div> </div>
@ -13361,7 +13364,7 @@ div#notebook {
<div class="output_text output_subarea output_execute_result"> <div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f9a2e1f4dd8&gt;</pre> <pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f0d22cd6f28&gt;</pre>
</div> </div>
</div> </div>
@ -13374,7 +13377,7 @@ div#notebook {
<div class="output_png output_subarea "> <div class="output_png output_subarea ">
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@ -13418,7 +13421,7 @@ div#notebook {
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<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div> <div class="prompt input_prompt">In&nbsp;[16]:</div>
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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> <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>
@ -13461,7 +13464,9 @@ div#notebook {
<span class="s1">&#39;head_mean_ang&#39;</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="s1">&#39;head_mean_ang&#39;</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="s1">&#39;head_mean_vec_len&#39;</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="s1">&#39;head_mean_vec_len&#39;</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="s1">&#39;spacing&#39;</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="s1">&#39;spacing&#39;</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="s1">&#39;orientation&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span> <span class="s1">&#39;orientation&#39;</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="s1">&#39;field_area&#39;</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="s1">&#39;theta_score&#39;</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="p">})</span>
<span class="k">return</span> <span class="n">result</span> <span class="k">return</span> <span class="n">result</span>
@ -13558,7 +13563,9 @@ div#notebook {
<span class="s1">&#39;head_mean_ang&#39;</span><span class="p">:</span> <span class="n">head_mean_ang</span><span class="p">,</span> <span class="s1">&#39;head_mean_ang&#39;</span><span class="p">:</span> <span class="n">head_mean_ang</span><span class="p">,</span>
<span class="s1">&#39;head_mean_vec_len&#39;</span><span class="p">:</span> <span class="n">head_mean_vec_len</span><span class="p">,</span> <span class="s1">&#39;head_mean_vec_len&#39;</span><span class="p">:</span> <span class="n">head_mean_vec_len</span><span class="p">,</span>
<span class="s1">&#39;spacing&#39;</span><span class="p">:</span> <span class="n">spacing</span><span class="p">,</span> <span class="s1">&#39;spacing&#39;</span><span class="p">:</span> <span class="n">spacing</span><span class="p">,</span>
<span class="s1">&#39;orientation&#39;</span><span class="p">:</span> <span class="n">orientation</span> <span class="s1">&#39;orientation&#39;</span><span class="p">:</span> <span class="n">orientation</span><span class="p">,</span>
<span class="s1">&#39;field_area&#39;</span><span class="p">:</span> <span class="n">fields_areas</span><span class="p">[</span><span class="n">fields</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">*</span> <span class="n">bin_size</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
<span class="s1">&#39;theta_score&#39;</span><span class="p">:</span> <span class="n">theta_mod_idx</span><span class="p">(</span><span class="n">spike_times</span><span class="o">.</span><span class="n">times</span><span class="o">.</span><span class="n">magnitude</span><span class="p">,</span> <span class="n">binsize</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">time_limit</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="p">})</span> <span class="p">})</span>
<span class="k">return</span> <span class="n">result</span> <span class="k">return</span> <span class="n">result</span>
@ -13579,15 +13586,14 @@ div#notebook {
<div class="output_subarea output_stream output_stderr output_text"> <div class="output_subarea output_stream output_stderr output_text">
<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. <pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: Mean of empty slice.
warnings.warn(&#34;Instantaneous firing rate approximation contains &#34;
</pre> </pre>
</div> </div>
</div> </div>
<div class="output_area"> <div class="output_area">
<div class="prompt output_prompt">Out[&nbsp;]:</div> <div class="prompt output_prompt">Out[16]:</div>
@ -13597,21 +13603,23 @@ div#notebook {
speed_score -0.068927 speed_score -0.068927
out_field_mean_rate 1.857990 out_field_mean_rate 1.857990
in_field_mean_rate 5.257561 in_field_mean_rate 5.257561
max_field_mean_rate 8.882211 max_field_mean_rate NaN
max_rate 23.006163 max_rate 23.006163
sparsity 0.466751 sparsity 0.466751
selectivity 7.153172 selectivity 7.153172
interspike_interval_cv 3.807699 interspike_interval_cv 3.807699
burst_event_ratio 0.398230 burst_event_ratio 0.398230
bursty_spike_ratio 0.678064 bursty_spike_ratio 0.678064
gridness -0.466923 gridness -0.466836
border_score 0.029328 border_score 0.029328
information_rate 1.009215 information_rate 1.009215
information_specificity 0.317256 information_specificity 0.317256
head_mean_ang 5.438033 head_mean_ang 5.438033
head_mean_vec_len 0.040874 head_mean_vec_len 0.040874
spacing 0.628784 spacing 0.628784
orientation 20.224859 orientation 69.775141
field_area 0.412306
theta_score -0.430279
dtype: float64</pre> dtype: float64</pre>
</div> </div>
@ -13649,13 +13657,13 @@ dtype: float64</pre>
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@ -13667,13 +13675,16 @@ var element = $('#a40b72ae-f0e6-42c9-aab9-adcae33d48c4');
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<pre>/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2 <pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: Mean of empty slice.
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:110: RuntimeWarning: invalid value encountered in long_scalars
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2 /home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) * return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply /home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) * return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/apps/expipe-project/spike-statistics/spike_statistics/core.py:27: RuntimeWarning: invalid value encountered in double_scalars
return (pk - th)/(pk + th)
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:112: RuntimeWarning: invalid value encountered in long_scalars
</pre> </pre>
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<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">debug</span>
</pre></div>
</div>
</div>
</div>
</div> </div>
<div class="cell border-box-sizing code_cell rendered"> <div class="cell border-box-sizing code_cell rendered">
<div class="input"> <div class="input">

64
actions/calculate-statistics/data/10_calculate_spatial_statistics.ipynb
View File

@ -19,7 +19,9 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"17:02:17 [I] klustakwik KlustaKwik2 version 0.2.6\n" "20:57:48 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:25: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
] ]
} }
], ],
@ -45,6 +47,7 @@
"import septum_mec\n", "import septum_mec\n",
"import scipy.ndimage.measurements\n", "import scipy.ndimage.measurements\n",
"from distutils.dir_util import copy_tree\n", "from distutils.dir_util import copy_tree\n",
"from spike_statistics.core import theta_mod_idx\n",
"\n", "\n",
"from tqdm import tqdm_notebook as tqdm\n", "from tqdm import tqdm_notebook as tqdm\n",
"from tqdm._tqdm_notebook import tqdm_notebook\n", "from tqdm._tqdm_notebook import tqdm_notebook\n",
@ -208,7 +211,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9a2e1f4dd8>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d22cd6f28>"
] ]
}, },
"execution_count": 6, "execution_count": 6,
@ -217,7 +220,7 @@
}, },
{ {
"data": { "data": {
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"image/png": 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"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
@ -258,17 +261,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": { "metadata": {},
"scrolled": false
},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "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", "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: Mean of empty slice.\n"
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
] ]
}, },
{ {
@ -278,25 +278,27 @@
"speed_score -0.068927\n", "speed_score -0.068927\n",
"out_field_mean_rate 1.857990\n", "out_field_mean_rate 1.857990\n",
"in_field_mean_rate 5.257561\n", "in_field_mean_rate 5.257561\n",
"max_field_mean_rate 8.882211\n", "max_field_mean_rate NaN\n",
"max_rate 23.006163\n", "max_rate 23.006163\n",
"sparsity 0.466751\n", "sparsity 0.466751\n",
"selectivity 7.153172\n", "selectivity 7.153172\n",
"interspike_interval_cv 3.807699\n", "interspike_interval_cv 3.807699\n",
"burst_event_ratio 0.398230\n", "burst_event_ratio 0.398230\n",
"bursty_spike_ratio 0.678064\n", "bursty_spike_ratio 0.678064\n",
"gridness -0.466923\n", "gridness -0.466836\n",
"border_score 0.029328\n", "border_score 0.029328\n",
"information_rate 1.009215\n", "information_rate 1.009215\n",
"information_specificity 0.317256\n", "information_specificity 0.317256\n",
"head_mean_ang 5.438033\n", "head_mean_ang 5.438033\n",
"head_mean_vec_len 0.040874\n", "head_mean_vec_len 0.040874\n",
"spacing 0.628784\n", "spacing 0.628784\n",
"orientation 20.224859\n", "orientation 69.775141\n",
"field_area 0.412306\n",
"theta_score -0.430279\n",
"dtype: float64" "dtype: float64"
] ]
}, },
"execution_count": 9, "execution_count": 16,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -342,7 +344,9 @@
" 'head_mean_ang': np.nan,\n", " 'head_mean_ang': np.nan,\n",
" 'head_mean_vec_len': np.nan,\n", " 'head_mean_vec_len': np.nan,\n",
" 'spacing': np.nan,\n", " 'spacing': np.nan,\n",
" 'orientation': np.nan\n", " 'orientation': np.nan,\n",
" 'field_area': np.nan,\n",
" 'theta_score': np.nan\n",
" })\n", " })\n",
" return result\n", " return result\n",
"\n", "\n",
@ -439,7 +443,9 @@
" 'head_mean_ang': head_mean_ang,\n", " 'head_mean_ang': head_mean_ang,\n",
" 'head_mean_vec_len': head_mean_vec_len,\n", " 'head_mean_vec_len': head_mean_vec_len,\n",
" 'spacing': spacing,\n", " 'spacing': spacing,\n",
" 'orientation': orientation\n", " 'orientation': orientation,\n",
" 'field_area': fields_areas[fields].mean() * bin_size**2,\n",
" 'theta_score': theta_mod_idx(spike_times.times.magnitude, binsize=0.01, time_limit=0.2)\n",
" })\n", " })\n",
" return result\n", " return result\n",
" \n", " \n",
@ -449,14 +455,12 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"scrolled": false
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "2372ae1b2bea435dbc5bbfe2453747a6", "model_id": "b2117ea9b6044c22abd353d0f9d774a7",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
@ -471,13 +475,16 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n", "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: Mean of empty slice.\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:110: RuntimeWarning: invalid value encountered in long_scalars\n",
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\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", " 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 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", "/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" " return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
"/home/mikkel/apps/expipe-project/spike-statistics/spike_statistics/core.py:27: RuntimeWarning: invalid value encountered in double_scalars\n",
" return (pk - th)/(pk + th)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:112: RuntimeWarning: invalid value encountered in long_scalars\n"
] ]
} }
], ],
@ -487,15 +494,6 @@
" left_index=True, right_index=True)" " left_index=True, right_index=True)"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%debug"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -598,5 +596,5 @@
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 4
} }

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actions/stimulus-spike-lfp-response-direct/attributes.yaml Normal file
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registered: '2021-01-23T10:44:56'
data:
results: results.csv
notebook: 10-calculate-stimulus-spike-lfp-response-direct.ipynb

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actions/stimulus-spike-lfp-response-direct/modules/parameters.yaml Normal file
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theta_band_f1: 6
theta_band_f2: 10