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auto commit actions/waveform-analysis
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
analysis update
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"08:31:25 [I] klustakwik KlustaKwik2 version 0.2.6\n"
auto commit actions/waveform-analysis
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]
}
],
"source": [
"import os\n",
"import expipe\n",
"import pathlib\n",
"import numpy as np\n",
"import spatial_maps.stats as stats\n",
"import septum_mec\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"import head_direction.head as head\n",
"import spatial_maps as sp\n",
"import speed_cells.speed as spd\n",
"import re\n",
"import joblib\n",
"import multiprocessing\n",
"import shutil\n",
"import psutil\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from distutils.dir_util import copy_tree\n",
"from neo import SpikeTrain\n",
"import scipy\n",
"\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
auto commit actions/waveform-analysis
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"\n",
"from spike_statistics.core import permutation_resampling\n",
"\n",
"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
"\n",
"from septum_mec.analysis.plotting import violinplot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"color_control = '#4393c3'\n",
"color_stimulated = '#d6604d'\n",
"\n",
"color_bs = '#5aae61'\n",
"color_ns = '#9970ab'\n",
"\n",
"figsize_violin = (1.7, 3)\n",
"figsize_gen = (4, 3)\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"waveform-analysis\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
"output_path.mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data()\n",
"actions = data_loader.actions\n",
"project = data_loader.project"
]
},
{
"cell_type": "code",
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"execution_count": 5,
auto commit actions/waveform-analysis
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"N cells: 1284\n"
auto commit actions/waveform-analysis
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]
}
],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
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"units = pd.read_csv(identify_neurons.data_path('units'))\n",
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"session_units = pd.merge(sessions, units, on='action')\n",
"#########################3\n",
"# session_units = session_units.drop_duplicates('unit_id')\n",
"#################################\n",
"print('N cells:',session_units.shape[0])"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>action</th>\n",
" <th>baseline</th>\n",
" <th>entity</th>\n",
" <th>frequency</th>\n",
" <th>i</th>\n",
" <th>ii</th>\n",
" <th>session</th>\n",
" <th>stim_location</th>\n",
" <th>stimulated</th>\n",
" <th>tag</th>\n",
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" <th>date</th>\n",
" <th>entity_date</th>\n",
" <th>Hz11</th>\n",
" <th>Hz30</th>\n",
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" <th>channel_group</th>\n",
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" <th>max_depth_delta</th>\n",
" <th>max_dissimilarity</th>\n",
" <th>unit_id</th>\n",
" <th>unit_idnum</th>\n",
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" <th>unit_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1849-060319-3</td>\n",
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" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
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" <td>60319</td>\n",
" <td>1849-060319</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
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" <td>1</td>\n",
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" <td>100</td>\n",
" <td>0.05</td>\n",
" <td>f129d848-ebee-4555-965f-420e402eb820</td>\n",
" <td>703</td>\n",
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" <td>104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1849-060319-3</td>\n",
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" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
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" <td>60319</td>\n",
" <td>1849-060319</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
auto commit actions/waveform-analysis
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" <td>1</td>\n",
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" <td>100</td>\n",
" <td>0.05</td>\n",
" <td>b50f3878-32aa-40e9-8753-9f401ef32a23</td>\n",
" <td>704</td>\n",
auto commit actions/waveform-analysis
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" <td>108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
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" <td>60319</td>\n",
" <td>1849-060319</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
auto commit actions/waveform-analysis
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" <td>1</td>\n",
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" <td>100</td>\n",
" <td>0.05</td>\n",
" <td>b1626e1a-0469-4ee0-a812-88ee18accd39</td>\n",
" <td>705</td>\n",
auto commit actions/waveform-analysis
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" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
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" <td>60319</td>\n",
" <td>1849-060319</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
auto commit actions/waveform-analysis
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" <td>1</td>\n",
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" <td>100</td>\n",
" <td>0.05</td>\n",
" <td>feb62d54-f173-4a3c-939c-8c1465e71b0f</td>\n",
" <td>706</td>\n",
auto commit actions/waveform-analysis
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" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
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" <td>60319</td>\n",
" <td>1849-060319</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
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" <td>1</td>\n",
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" <td>100</td>\n",
" <td>0.05</td>\n",
" <td>3498a09b-b943-4917-866a-8910dbcfd0cd</td>\n",
" <td>707</td>\n",
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" <td>98</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
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" stim_location stimulated tag date entity_date Hz11 Hz30 \\\n",
"0 NaN False baseline ii 60319 1849-060319 False True \n",
"1 NaN False baseline ii 60319 1849-060319 False True \n",
"2 NaN False baseline ii 60319 1849-060319 False True \n",
"3 NaN False baseline ii 60319 1849-060319 False True \n",
"4 NaN False baseline ii 60319 1849-060319 False True \n",
"\n",
" channel_group max_depth_delta max_dissimilarity \\\n",
"0 1 100 0.05 \n",
"1 1 100 0.05 \n",
"2 1 100 0.05 \n",
"3 1 100 0.05 \n",
"4 1 100 0.05 \n",
"\n",
" unit_id unit_idnum unit_name \n",
"0 f129d848-ebee-4555-965f-420e402eb820 703 104 \n",
"1 b50f3878-32aa-40e9-8753-9f401ef32a23 704 108 \n",
"2 b1626e1a-0469-4ee0-a812-88ee18accd39 705 85 \n",
"3 feb62d54-f173-4a3c-939c-8c1465e71b0f 706 94 \n",
"4 3498a09b-b943-4917-866a-8910dbcfd0cd 707 98 "
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]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"session_units.head()"
]
},
{
"cell_type": "code",
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"execution_count": 7,
auto commit actions/waveform-analysis
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"action\n",
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"1833-010719-2 33\n",
"1833-010719-1 29\n",
"1833-290519-1 28\n",
"1833-020719-3 27\n",
"1849-280219-1 26\n",
" ..\n",
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"1849-110319-3 4\n",
"1839-060619-4 4\n",
"1839-060619-3 4\n",
"1839-120619-1 3\n",
"1839-060619-1 2\n",
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"Name: unit_name, Length: 87, dtype: int64"
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]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"# session_units.groupby('action').count().unit_name.hist()\n",
"session_units.groupby('action').count().sort_values('unit_name', ascending=False).unit_name"
auto commit actions/waveform-analysis
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Process all data"
]
},
{
"cell_type": "code",
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"execution_count": 8,
auto commit actions/waveform-analysis
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"metadata": {},
"outputs": [],
"source": [
"def features(row):\n",
" action_id = row['action']\n",
" channel_id = row['channel_group']\n",
" unit = row['unit_name']\n",
" template = data_loader.template(action_id, channel_id, unit)\n",
" spike_times = data_loader.spike_train(action_id, channel_id, unit)\n",
" half_widths, peak_to_troughs = calculate_waveform_features_from_template(\n",
" template.data, template.sampling_rate)\n",
" peak_amps = template.data.min(axis=1)\n",
" half_widths = half_widths * 1000 # to ms\n",
" peak_to_troughs = peak_to_troughs * 1000 # to ms\n",
" idxs = np.argsort(peak_amps)\n",
" peak_to_trough = np.nan\n",
" for p2t in peak_to_troughs[idxs]:\n",
" if np.isfinite(p2t) and p2t > .1:\n",
" peak_to_trough = p2t\n",
" break\n",
" half_width = np.nan\n",
" for hw in half_widths[idxs]:\n",
" if np.isfinite(hw):\n",
" half_width = hw\n",
" break\n",
" \n",
" return pd.Series({\n",
" 'half_width': half_width,\n",
" 'peak_to_trough': peak_to_trough,\n",
" 'average_firing_rate': float(len(spike_times) / spike_times.t_stop),\n",
" 'template': template.data[idxs[0]]\n",
" })"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"model_id": "39294999a86e4f8ea477c5180a80e8c6",
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
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"HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
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]
},
"metadata": {},
"output_type": "display_data"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
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}
],
"source": [
"results = session_units.merge(\n",
" session_units.progress_apply(features, axis=1), \n",
" left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> \u001b[0;32m/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/data_processing.py\u001b[0m(619)\u001b[0;36mtemplate\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 617 \u001b[0;31m lim=lim)\n",
"\u001b[0m\u001b[0;32m 618 \u001b[0;31m }\n",
"\u001b[0m\u001b[0;32m--> 619 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_templates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maction_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchannel_group\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0munit_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 620 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 621 \u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mspike_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannel_group\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> action_id\n",
"'1834-010319-3'\n",
"ipdb> channel_group\n",
"0\n",
"ipdb> unit_id\n",
"72\n"
]
}
],
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"source": [
"%debug"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [],
"source": [
"df = results.loc[:, ['half_width', 'peak_to_trough']].dropna()\n",
"\n",
"idxs_df = cluster_waveform_features(df.half_width, df.peak_to_trough)\n",
"\n",
"results.loc[df.index, 'bs'] = idxs_df"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"plt.figure(figsize=figsize_gen)\n",
"size = 5\n",
"mew = .5\n",
"marker_bs = '.'\n",
"marker_ns = '+'\n",
"\n",
"plt.scatter(\n",
" results.query('bs==0')['half_width'], \n",
" results.query('bs==0')['peak_to_trough'], \n",
" c=color_ns, s=size, marker=marker_ns, linewidth=mew, label='NS')\n",
"\n",
"plt.scatter(\n",
" results.query('bs==1')['half_width'], \n",
" results.query('bs==1')['peak_to_trough'], \n",
" c=color_bs, s=size, marker=marker_bs, linewidth=mew, label='BS')\n",
"\n",
"plt.xlabel('half width')\n",
"plt.ylabel('peak to through')\n",
"\n",
"plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
" mode=\"expand\", borderaxespad=0, ncol=2)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"clusters.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"clusters.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 12,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"plt.figure(figsize=figsize_gen)\n",
"\n",
"size = 5\n",
"mew = .5\n",
"marker_bs = '.'\n",
"marker_ns = '+'\n",
"\n",
"plt.scatter(\n",
" results.query('bs==0')['half_width'], \n",
" results.query('bs==0')['peak_to_trough'], \n",
" c=color_ns, s=size, marker=marker_ns, linewidth=mew, label='NS')\n",
"\n",
"plt.scatter(\n",
" results.query('bs==1')['half_width'], \n",
" results.query('bs==1')['peak_to_trough'], \n",
" c=color_bs, s=size, marker=marker_bs, linewidth=mew, label='BS')\n",
"\n",
"plt.scatter(\n",
" results.query('bs==1 and average_firing_rate > 10')['half_width'], \n",
" results.query('bs==1 and average_firing_rate > 10')['peak_to_trough'], \n",
" c='red', s=size, marker=marker_bs, linewidth=mew, label='BS rate > 10 Hz')\n",
"\n",
"plt.xlabel('half width')\n",
"plt.ylabel('peak to through')\n",
"\n",
"plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
" mode=\"expand\", borderaxespad=0, ncol=2)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"clusters_and_rate.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"clusters_and_rate.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 13,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"stim = results.query('stimulated').loc[:, ['half_width', 'peak_to_trough']].dropna()\n",
"\n",
"idxs_stim = cluster_waveform_features(stim.half_width, stim.peak_to_trough)\n",
"\n",
"results.loc[stim.index, 'bs_stim'] = idxs_stim"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 14,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"control = results.query('not stimulated').loc[:, ['half_width', 'peak_to_trough']].dropna()\n",
"\n",
"idxs_control = cluster_waveform_features(control.half_width, control.peak_to_trough)\n",
"\n",
"results.loc[control.index, 'bs_ctrl'] = idxs_control"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 15,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"plt.figure(figsize=figsize_gen)\n",
"size = 5\n",
"mew = .5\n",
"marker_bs = '.'\n",
"marker_ns = '+'\n",
"plt.scatter(\n",
" results.query('bs_stim==0')['half_width'], \n",
" results.query('bs_stim==0')['peak_to_trough'], \n",
" c=color_stimulated, s=size, marker=marker_ns, \n",
" linewidth=mew, label='Stimulated NS', alpha=.5)\n",
"\n",
"plt.scatter(\n",
" results.query('bs_stim==1')['half_width'], \n",
" results.query('bs_stim==1')['peak_to_trough'], \n",
" c=color_stimulated, s=size, marker=marker_bs, \n",
" linewidth=mew, label='Stimulated BS', alpha=.5)\n",
"\n",
"\n",
"plt.scatter(\n",
" results.query('bs_ctrl==0')['half_width'], \n",
" results.query('bs_ctrl==0')['peak_to_trough'], \n",
" c=color_control, s=size, marker=marker_ns, \n",
" linewidth=mew, label='Control NS', alpha=.5)\n",
"\n",
"plt.scatter(\n",
" results.query('bs_ctrl==1')['half_width'], \n",
" results.query('bs_ctrl==1')['peak_to_trough'], \n",
" c=color_control, s=size, marker=marker_bs, \n",
" linewidth=mew, label='Control BS', alpha=.5)\n",
"\n",
"plt.xlabel('half width')\n",
"plt.ylabel('peak to through')\n",
"plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
" mode=\"expand\", borderaxespad=0, ncol=2)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"compare-clusters.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"compare-clusters.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 16,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"results.average_firing_rate = results.apply(lambda x: float(x.average_firing_rate), axis=1)"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 17,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"results.frequency = results.apply(\n",
" lambda x: \n",
" float(x.frequency.replace('Hz', '')) if isinstance(x.frequency, str) \n",
" else float(x.frequency), axis=1)"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 18,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"bins=100\n",
"density=True\n",
"cumulative=True\n",
"histtype='step'\n",
"lw = 2\n",
"\n",
"plt.figure(figsize=figsize_gen)\n",
"plt.title('Narrow spiking')\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_ctrl==0')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_control, lw=lw, label='Control');\n",
"\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_stim==0')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');\n",
"\n",
"plt.xlim(-.5, 56)\n",
"# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
"# mode=\"expand\", borderaxespad=0, ncol=2)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_ns.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_ns.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"bins=100\n",
"density=True\n",
"cumulative=True\n",
"histtype='step'\n",
"lw = 2\n",
"\n",
"plt.figure(figsize=figsize_gen)\n",
"plt.title('Narrow spiking')\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_ctrl==0')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_control, lw=lw, label='Control');\n",
"\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_stim==0 and frequency==30')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');\n",
"\n",
"plt.xlim(-.5, 56)\n",
"# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
"# mode=\"expand\", borderaxespad=0, ncol=2)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_ns_30.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_ns_30.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"bins=100\n",
"density=True\n",
"cumulative=True\n",
"histtype='step'\n",
"lw = 2\n",
"\n",
"plt.figure(figsize=figsize_gen)\n",
"plt.title('Narrow spiking')\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_ctrl==0')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_control, lw=lw, label='Control');\n",
"\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_stim==0 and frequency==11')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');\n",
"\n",
"plt.xlim(-.5, 56)\n",
"# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc=\"lower left\",\n",
"# mode=\"expand\", borderaxespad=0, ncol=2)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_ns_11.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_ns_11.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"bins = 100\n",
"\n",
"plt.figure(figsize=figsize_gen)\n",
"plt.title('Broad spiking')\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_ctrl==1')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_control, lw=lw);\n",
"\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_stim==1')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_stimulated, lw=lw);\n",
"\n",
"plt.xlim(-.5, 44)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_bs.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_bs.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAADPCAYAAAAeTt+OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAOzUlEQVR4nO3dfbBcdX3H8fcNILEQUQvaNuMIFPmWKSO0WIIlQpgBBSrYUkYttZhYQadGVJyJBaVqizIVqLXSioVGqAPlGcSKBVMrQsDhMQgh/TIwI8P40AKVJyFAyO0f52xclt29e+8+nD1336+ZDHcf7tnfSTif/f5+53fOb2p6ehpJk21B1Q2QVD2DQJJBIMkgkIRBIAmDQBKwddUNUGcRsTPwAHB3+dRWwNPAiZm5dkifuSPwcGZODWBbRwIHZ+YJEfE94KzMvKzlPecCF2Xmmn4/T3NnEIy/ZzJz78aDiHgncB7whspa1KPMvBq4eob3vH9EzVEXBkH9/CrwU4CIWAZ8CfgFsB2wL/Be4ATgBeB/gJWZeV9E7A78I7A98BvAOuBdmbkxIo4CPkdRbdza6YMj4rPAHwHPAY8CyzPzpxGxCfh74KCyHSdn5hURsRw4OjPf3rSNrYELgefLtq4BzgJuA/4TuAZYArwa+GRmXhwRvwKcDewHPAbcC5CZy+f0N6iXcIxg/L08ItaVfx6kOPBPa3p9T+BPMnMvYH9gFXBQ+fhC4KqImAKOA87PzDcDuwG7AH8QEa8FVgN/nJn7AA+2a0REvA74KPB7mfkm4DqKAxaKLsv/lb//TmB1ROzUZjMvAy4F/hd4T2Zuanl9V+DazNwX+ATwhfL5Uyi+tH4LOBj4ne5/ZZotg2D8PZOZe5d/Xg8sAy6KiF3K1x/KzMbBeyhwcWY+DJCZ5wGLgZ0pDqyHI2IV8BWKqmB7YClwd2beW27jqx3a8WPgLuCOiDgDWJeZVzW9flb5mT+kGNM4oM02zgQOAf4mM9vNbX+eoiIAuIOiKgA4HPiXzNycmU8A53doo+bIIKiZzLwJSIpuAMBTTS+3+/ecArYB/g04nuIb/4sUB9oUMF3+t6H1W7rxuZuBA4HlFN2CL0bElzr83gKKrkmrr1OE0DntPgN4rvwcWtq1qaWN7batPhgENVP29XcH7mzz8rXAuxpleUSsoDho7wfeBvx1Zl5McZAtoSjpbwB+OyL2KrexvMPn7gXcA2zIzNMowmSvprccW77vdylK+OvbbOYWijJ/t4g4rsddBvgWsCIiFpTjBceU+6ABMQjGX/MYwTrgMuD4zLyv9Y2Z+R2KA/S7EbGeYjDu7eW37MnAlRFxG8XA2/XAbmU34hjggoi4g2Ls4CUy8y7gEuC2chvvAz7W9Jb9y99fTTEI+fMO29lIETanR8Rv9vh3cBqwkaLLsYZijOHpHn9XPZjyMmT1KyKmgZ0y85Ehbf/dwBOZeU1ELAAuB67LzK8M4/MmkRWB6uAe4JNlRXQP8BPg3GqbNL9YEUiyIpBkEEjCIJBEBdcaLFmyZHrx4sWj/lhp4q1fv/6RzGw39Xv0QbB48WKuuOKKUX+sNPHKa1XasmsgySCQ1GMQRMSS8g4zrc8fERG3RsTNs5w7LmmMzBgE5WWr5wILW57fhmJe+1sprko7vry2XVLN9DJY+ABwFMUlpM32AO5vXFwSETdSXIN+6UBbKE2IDSuOrOyzZwyCzLy8vIlmq1cAjzc9fhLYYUDtkip38Fk3AvDl278wwzvrr5/Th08Ai5oeL6K4n5w09hoHeTdVBcCH91k1nA3f9OcdX+onCDYAb4iIV1PcJecA4Iw+tif1ZTYH95fnsP09vtb1hswDM6z7ukeXnZ51EETEMcD2mfnPEXEixV1xFgCrM/PHc22kNBfNB/+wvsFHFQBV6ikIMvNHFLeSJjMvbHr+m8A3h9IyqUmn/vpcvtlhMg7u2XBdA42ddt/yszngPchnzyBQ5Xrp27fyYB8sg0CVmc3pOQ/84TIINFKjGNzT7BkEGorWcr+Xvr7f+tUxCDRQnQKgGwOgegaBRsqDfjwZBOpbu1H/1krAABhv3phEkhWBZq/Tef81K5e+5FJaK4F6MAjUs04B0OgGbFjh6cC6Mgg0o5kCoB0rgXoxCDRrnQLAg7++DAK11a4KWLNyKfDSLoABUH8GgV6k2wVADgTOXwaBumpUAeBg4HxmEAh4aSXw4gCwEpjvnFAkyYpg0nWrBFpZCcxfBoHaqnKxDY2eQSCgeyWg+c8gmECeIlQrg0CAXYFJZxBMkO6nCJ0tOMlmDIKIWAD8E7AX8Czw/sy8v+n1jwPHAJuBz2fmlUNqq+bIroBm0ktF8IfAwsx8c0TsB5wJvAMgIl4JfATYDdgOWAcYBGPOgUG16iUIlgL/AZCZP4iINzW99gvgQYoQ2I6iKtCY6HQj0XZTha0EJlsvQfAK4PGmxy9ExNaZual8/BBwL7AVcNqA26c5mMvKQZpsvQTBE8CipscLmkLgMODXgV3Kx9dGxNrMvGWAbdQced8A9aqXaw3WAocDlGMEdze99nPgGeDZzNwIPAa8ctCNlDRcvVQEVwKHRMRNwBSwIiJOBO7PzKsj4mDgBxGxGbgR+M7wmqtOui0lZgWgmcwYBJm5Gfhgy9P/3fT6p4FPD7hdkkbICUU1121FYSsB9cr7EUiyIqg7xwM0CAZBTW3pElTcDs0PBsE8YSWgfhgENdNtcFCaKwcLJVkR1I2DgxoGg6AGXjRrsMJ2aP4yCGrKSkCDZBCMMQcGNSoOFkqyIhhHnSoBuwMaFoNgjHhnIVXFIBgDne4t2GAloGFzjECSFUFV2nUDrARUFSsCSVYEVVuzcqmrDalyBsGIeWZA48ggGCNWAqqKQTAis1l+TBo1g2DEvG5A42gQy6IfRrGuwRRwO/ChzJweTnPrZ6YxAbsDGgf9Lou+CDgdWJaZj0TEKmBH4OFhNbiunCOgcdbvsui/T7EW4pkRsStwbmYaAnh2QPXS77LoOwIHAXsDTwE3RMTNmXnf4Js6/pwtqLrqZWZht2XRHwVuzcyfZeZTwPcpQkFSjfRSEawFjgAuabMs+h3AnhGxI8WS6PsB5wy8lWOutRJYs3Lplp8bpwetBDTOBrEs+knAteV7L8nMe4bUVklDMohl0S8CLhpwu2qheyVwZOvbpbHlhKI58IyA5huDYAAalUC7KsCxAdWBQdCH5q6AVGcGQY+6dQe8n4DqzjsUSbIimK1u3QErAdWVQdAHTxFqvjAI5sAA0HxjEMyglzkDdglUdwZBj9rdWswA0HzhWQNJVgSddOoSWAVoPrIikGRFMBPvOqxJYBC08MpCTSKDoAPvNahJYhCUrAQ0yQyCGVgJaBJMdBC0qwJ+eZMRBwk1OSY6CNrxOgJNIoOA9rcflyaJQdCBYwOaJM4slNT/suhN7/kW8I3MPHsYDR2kdoOEjg1okvW1LHqTU4FXDbpxw9bu0mJpEvW7LDoRcTSwufGeunNsQJOor2XRI2JP4BjgaOCvhtHAQWp0CZw+LL1YL0HQbVn0Y4HFwHeBnYHnIuJHmTkvqgNpUvS1LHpmrmr8HBGfAX5WpxCwEpAKfS+LPtTWDUinLoGkQt/Loje97zMDapOkEZvXMwsdHJR648xCSfOvImieNWglIPXGikDS/KkIWq8fWLNy6Zapw1YCUndWBJLmT0XQ4IVE0uxZEUiqf0XgGoVS/6wIJNW/ImjwOgJp7qwIJM2fiqDBsQFp9mobBF5aLA2OXQNJ9a0IWtklkOautkFgl0AanFoFgYuQSMNRqyBoxy6B1L9aBoEHvzRYnjWQZBBIqknXYMvkoYrbIc1XfS+LHhEfA95dPrwmMz87jIZKGp6+lkWPiF2BPwWWUKyIfGNEXJmZPxxkI50zIA1Xv8uiPwQcmpkvAETENsDGQTXOLoE0Gn0ti56ZzwOPRMQUcDpwZ2beN4yGgqcNpWHp5axBt2XRiYiFwAXle/5isM2TNAq9BMFa4HCA1mXRy0rgG8BdmfmBRhdBUr30tSw6sBVwILBtRBxWvv+kzLx5KK2VNBS
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"bins = 100\n",
"\n",
"plt.figure(figsize=figsize_gen)\n",
"plt.title('Broad spiking')\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_ctrl==1')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_control, lw=lw);\n",
"\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_stim==1 and frequency==11')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_stimulated, lw=lw);\n",
"\n",
"plt.xlim(-.5, 44)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_bs_11.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_bs_11.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"bins = 100\n",
"\n",
"plt.figure(figsize=figsize_gen)\n",
"plt.title('Broad spiking')\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_ctrl==1')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_control, lw=lw);\n",
"\n",
"_, bins, _ = plt.hist(\n",
" results.query('bs_stim==1 and frequency==30')['average_firing_rate'], \n",
" bins=bins, density=density, cumulative=cumulative, \n",
" histtype=histtype, color=color_stimulated, lw=lw);\n",
"\n",
"plt.xlim(-.5, 44)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_bs_30.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"cumulative_bs_30.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 135025.0 p value 0.11036062942886643\n",
"U-test: U value 6176.0 p value 0.12484872783599776\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 122.4x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAADPCAYAAAA5xQlPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO2deZhcZZnof985VdVVvWTpdFYSkpDAxx5AZVGEMBpBUcYLMuNwnVFzEZmrz+ijPg46cEcHnTtzcWYcZVCBq0EdBBUQUZawJU4CBBISiCT5su9LdyfpdKe7az3f/eOc0qJTXcupszW3fs+TJ1Vd53znPVXveb/tXYTWmiZNjLAFaBINmorQBGgqQhOHpiI0AZqK0MShqQhNAIiFLQCAlHIOsAP4lFLq3pK/fwk4Wyn1iZBEaxgp5b3AA8BW4PdKqfYRn88AfqmUemcY8hWJkkWwgG9JKU8LWxAvUUrdqJR6psLn+8NWAoiIRXAYBv4F+JmU8hKlVLb0Q0dB/gNoB2YA64A/V0qlpZQZ4FFgAfDfgZUj3qeAO4BWIAvcCjwNHAQuUUptlVLeAvy1Umq2c72ngX9TSj1eIsM04MdAl/On3yqlbpNSfgL4C+wH6yRgH/BxpdR+KeUy4E5gdUk7ZwCPA18A1uJYCinl14A5wHRgNtDj3ON+KeWFwF1AAtjmfP4FpdSyur/pMkTJIgB8ExgE/rHMZ58C7lNKXQLMB+YCVzufJYDHlFJSKbW69D12l/NL4HNKqXOBjwM/BU4GHgOuctq4CkhIKU+TUo4HzgNGPsmfArYrpS4A3g2c6hwL8C7gM0qpM4E1wHfK3aCU8mznujcqpR4pc8i7geuVUqcDR4FPSyljwEPAbc49fMeRzzMipQhKKQv4GPBJKeWiER//LdAjpfwy8D1sq1Da3/7XiOOL7y8CtiqlVjnXeAPbYiwEHgHeL6XswH4K7wcWAR8AnhxplYAngeuklI8DnwZuUUodcz5bqpTa7Ly+B7iyzC22AM8D65RSz47yNSxTSvU7r9cCncA5juxPOP8/D/x+lPNdESlFAFBK7QZuBu7jjyYY4GfATcAu4N+AVwFR8vnxEU0V35e7RwOIY3cPb8e2LMuc9+8DrsF+AkfK9gq2Jbob24S/LKUs9u/5Ee0XRrnFDwMXSCmvHeXz4ZLXGvse87z5XqnQvisipwgASqlfAE8Any/585XAPyilHsT+gi4CzBqaewmQTh+LlPIs4DLsJy8NLAf+HljqvL4E2zw/ObIhKeU/YZvnXwGfA94AioPb90gpT3Je34xt/keSUUqtBBYD33fGHLWwEchIKa9y5LgQ20p4tmMYSUVw+Bvsp7/IV4FHpJSrge9j/2jzqzWilOoFrge+K6Vcj23+P1lixh/B/jGfU0oNA68BKx0lGcm3gfOklL/HHvztwLZUAHuBn0gpN2Jbi8+XOb8o0zLsKeUPq8nvHJ8HrgO+JqVcC3wRe6A7VMv5tSCa29CN48waPqKU+qCP17gD+JZS6pCUcha2wp6ilOrzov0oTR+bVGYX8KyUMoc9XrjRKyWApkVo4tC0CCNwzPwHsBef5gH/7Lz+OPbq5ytKqb8JTUCfiPJgMUzGO/39NcAtwCeBzzqLWRudBZ63FG+5G/KIdc7/e4Ak8BHgS1LKucCLnDinH/torX3/t3jxYj1WeOihh/Qdd9yhtdY6nU7rK664Qt9+++06nU5rrbVevHixXrVqVZgi1ktNv1EgFuHo0aNBXMY3pJTccMMNtLW1MXXqVBYsWBC2SJ5T06xBSvkV7P4ygb0DthxYgr2y9XvszRZrtPOvvfZa/fDDD3shb5P6qakbqzpYlFIuBN6Jvbt2OTAL+FfgVqXUu50L/alrMZtEglpmDVcC67GXYh8DfgO8DdsqgL0n8F5fpGsSGLWMEbqwnSA+iL3z9mvAUEoV+5QBYPwo5zYZI9SiCIeBTc7evJJSprG7hyIdgGdLnfVy9913c//994d1+Ya54YYbuOmmm8IWo6auYQVwlZRSOI6Wbdhr3gudz9/PiU4hgXH//fezbt266gdGkHXr1kVGiataBKXUb6SUlwEvYyvOZ7C3X++RUiaw98p/6auUVTjvvPNYtmxZmCK4YuHChWGL8AdqWkdQSn25zJ8v91iWJiHS3GtoArwF9hoWL14ctgiuiZLsgfgjNFcWQ8WblcUm/38wZruGjXuPcccj6ymM2OE4Z/YEvvThs8MRagwzZhVhzbZe1N5+2lJ/vAWtNXt7B/nCNWdhGG89lwE/GbNdw4bdfSTiBsm4+Yd/qUQMjeZA33D1Bpq8iTGrCGpfPy3xE8UXQrDj0EAIEo1txqQiHE/nODyQIW6eKH42b7F5X3+Zs5pUYkwqwrYDA5imQIgTxwEJ02DtjiMhSDW2GZOKoPYdI58vv/7REjdQe49hWc14jXoYk4rwytbDlOkVAIiZBrmCxd7Dg8EKNcYZc4pgWZr1u46STFQIhBb2OkOT2hlzirD38CCZXIHYaCYBW1le2dIboFRjnzGnCK/vPFo1KUAqEePlLb0EsY/yVmHMKcKKjd1Vd1HipmBgOMf+I56lD3jLM6YUIV+wWLPtMKlK4wPsRSWtYc3WwwFJNvapaa9BSvkqUFyl2QH8APh37Nw+S5VSX/dHvDezYc8xLK2JmdUz5hgGPLv+INdcdHIAko19qiqClDIJCKXUwpK/rcNO5bId+K2U8nyl1FrfpHRYsfEQubxl5yarQmsixus7jzCUydPaMmb31gKjlm9oAdAqpVzqHP81oEUptQ1ASvkUdoCLr4qgtebpdfurdgtFDMNeeVyz7TDvPnOqn6LVTWE4Te7YicvgwjRomdxV5gz/qUURhoBvAfcCp2JHNpXGMQwAp3gv2pvZcmCAvqEs7XU83ZaleWLN3sgpwoZv/Cvdz/4OMaKL05bFhUvupEPOC1ymWr7VzdgJKzWwWUp5DDsJZJFAAlyeeW0/VkGX3V8YjbZkjJc293I8naM9GfdRutrRWnPkpdWYra0Y8Td//bm+Y/StWx+KItQya1iMnSO5mEm8FRiUUs6TUgrs2EhfA1wKlua3q/eSqrOvNw2B1poVGw75JFn9DO3eS344fYISAIhYjJ7fvRiCVLUpwv8FJkgpVwAPYivGjcB/Yge9rC2mt/WLNVt7Gc7mScTqn+0aQvDQi7t9kModR1evA6t8BgEzlaTvtTewsrmApaot0ikL3FDmo4u9F6c8v3xx12jfXVVaW0y2HOhnV89xZk9ur36Cz3Q/vxJhlFfo4pjh2BubmHj+OUGKFf0Fpe5jw7yypZf2lLspoL24pPnVqvCtQiGdoW/teszW1KjHWLkcvSt9NbBlibwiPLpqN5Zlm3i3tLXE+M0rexnK5Ksf7CN9a9cDnDBbKMVMJjm0dHng+ySRVoR0tsBDL+6mtaW2tYPRKPooPLV2v0eSuePQM8ux8pWV0WhJkOnpZWj33oCkcq4b6NXq5OnX9pPJFYi7GCSOJGEa/GTZVgoheS5ZuRyHnv0dsfa2iscVu7Ke51cGJJlNZBWhYGmWPLe1rIOqG5IJk6PHs6FNJY++uh6dzWHEqo91zESCfY8+GWj3EFlFWLHhEEcGMpU9kerEEIIfPrslFD+FA489hVWordaGkWwhfbCbwW07/RWq9JqBXakOLEtz7zNbPI9Wam0x2d0zyOptwW5P54eG6F62knhHbdNXIQRYBQ48PmpxOM+JpCKs3trL3t5BWj20BsAflqfvfXpzoFahe9kL6IJVcbYwErO1lX2/egIrF8ziUuQUQWvNPU9vQQjq2leolfZkDLWvn/W7gsv/tednDyPqHOsYiThWOs3hl9b4JNWI6wVylTp4bedRth7op80nH4I/WIVnNlc50huOb93B8S07Ki4ijYYGdv80mPRUkVOEe5baP5Af1qBIRzLG6zuPBuLyvufBR9G6vl3TIrH2Nvpee4PBXXt8kOzNREoRNu7p4409fbQn/fUoEkKAhiXPbfH1Orlj/Rx4/BniVdYORsOW02LPg496LNmJREoRljy3FbS/1qBIeyrGqs2
"text/plain": [
"<Figure size 122.4x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"plt.figure(figsize=figsize_violin)\n",
"# test = 'permutation_resampling'\n",
"test = 'mann_whitney'\n",
"\n",
"plt.title('Broad spiking')\n",
"violinplot(\n",
" results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(), \n",
" results.query('bs_stim==1')['average_firing_rate'].to_numpy(), \n",
" test=test)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"rates_bs.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"rates_bs.png\", dpi=600, bbox_inches=\"tight\")\n",
"\n",
"plt.figure(figsize=figsize_violin)\n",
"plt.title('Narrow spiking')\n",
"violinplot(\n",
" results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(), \n",
" results.query('bs_stim==0')['average_firing_rate'].to_numpy(), \n",
" test=test)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"rates_ns.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"rates_ns.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 85419.0 p value 0.4550292852318226\n",
"U-test: U value 3843.0 p value 0.06623560612855536\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 122.4x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAADPCAYAAAA5xQlPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO2deZQcV33vP1XV3bNrpBnttmxt1vWK5AUs7zJgbMwSng0J+JFgFAPOgxM4hkOA2AFCyEueyUsChAB2wAQiIEYWeLcly7KRLMuWrJFsWbral9Fom9GMZu+l6r4/qtq0Rz3T3dW3q2r8+nOOjrqnq279qvtbt27d+1sMpRRVqphhG1AlGlSFUAWoCqGKR1UIVYCqEKp4VIVQBYBY2AYACCFmA/uAT0kp78/5+5eAC6WUt4dkWtkIIe4HfgXsBl6TUjaO+Hwm8Bsp5ZVh2JclSj2CA3xHCLEgbEN0IqW8Q0q5aozPO8IWAUSkR/AYAv4J+KUQ4gopZSr3Q08g/wY0AjOBNuBPpJTDQogk8DtgIfA/gXUj3tcB9wL1QAq4G1gJHAWukFLuFkJ8BfgLKeXZ3vFWAv8spXw8x4bpwH8Ck70/PSalvEcIcTvwMdwL6wzgMPAJKWWHEGIN8H1gY0475wGPA3cBm/F6CiHEN4DZwAzgbOCEd44dQoh3AD8AEsAe7/O7pJRrSv6m8xClHgHg28AA8Pd5PvsU8DMp5RXAfGAO8D7vswTwiJRSSCk35r7HveX8Bvi8lPJtwCeAXwBnAY8AN3lt3AQkhBALhBDNwCJg5JX8KWCvlPIS4BrgHG9bgKuAz0opzwc2Ad/Nd4JCiAu9494hpVyRZ5NrgI9IKc8FuoHPCCFiwHLgHu8cvuvZp41ICUFK6QAfBz4phLhhxMd/BZwQQnwZ+HfcXiH3fvv7Edtn318O7JZSbvCOsQ23x1gCrADeK4Rowr0KlwE3ADcDT47slYAngVuFEI8DnwG+IqU85X32tJRyp/f6PuDGPKdYAzwLtEkpnxnla1gjpez1Xm8GWoCLPNuf8P5/FnhtlP19ESkhAEgpDwJ3Aj/jD10wwC+BTwMHgH8GXgGMnM/7RzSVfZ/vHE0gjnt7uAy3Z1njvX8P8EHcK3CkbS/j9kQ/xu3CXxJCZO/vmRHt26Oc4oeAS4QQt4zy+VDOa4V7jhnefK6M0b4vIicEACnlg8ATwBdy/nwj8LdSyl/jfkGXA1YRzb0ICO8eixDiAuBa3CtvGHgO+DrwtPf6Ctzu+cmRDQkh/gG3e/4t8HlgG5Ad3L5LCHGG9/pO3O5/JEkp5TpgKfBDb8xRDNuBpBDiJs+Od+D2EtpWDCMpBI+/xL36s3wNWCGE2Aj8EPdHm1+oESllJ/AR4HtCiFdxu/9P5nTjK3B/zNVSyiFgC7DOE8lI/gVYJIR4DXfwtw+3pwJoB34uhNiO21t8Ic/+WZvW4D5S/qSQ/d72GeBW4BtCiM3AF3EHuoPF7F8MRnUZuny8p4YPSynfX8Fj3At8R0p5TAgxC1ewc6WUPTraj9LjY5WxOQA8I4RI444X7tAlAqj2CFU8qj3CCLxu/mbcyad5wD96rz+BO/v5spTyL0MzsEJEebAYJs3e/f6DwFeATwKf8yaztnsTPG8p3nInpIk27/9DQC3wYeBLQog5wHpOf6Yf/yilKv5v6dKlarywfPlyde+99yqllBoeHlbXX3+9+ta3vqWGh4eVUkotXbpUbdiwIUwTS6Wo3yiQHqG7uzuIw1QMIQS33XYbDQ0NTJs2jYULF4ZtknaKemoQQnwV936ZwF0Bew54AHdm6zXcxRZntP1vueUW9dBDD+mwt0rpFHUbKzhYFEIsAa7EXV27DpgF/F/gbinlNd6B/si3mVUiQTFPDTcCr+JOxT4CPApcitsrgLsm8O6KWFclMIoZI0zGdYJ4P+7K28OAKaXM3lP6gOZR9q0yTihGCF3ADm9tXgohhnFvD1maAG1TnaXy4x//mGXLloV1+LK57bbb+PSnPx22GUXdGtYCNwkhDM/RsgF3znuJ9/l7Od0pJDCWLVtGW1tb4Q0jSFtbW2REXLBHkFI+KoS4FngJVzifxV1+vU8IkcBdK/9NRa0swKJFi1izZk2YJvhiyZIlYZvwBkXNI0gpv5znz9dptqVKiFTXGqoAb4G1hqVLl4Ztgm+iZHsg/gjVmcVQ0TOzWOX/D8btrWF7+ynuXfEq9ogVjovOnsiXPnRhOEaNY8atEDbt6US299JQ94dTUErR3jnAXR+8ANN867kMVJJxe2t4/WAPibhJbdx6419dIoZCcaRnqHADVd7EuBWCPNxLTfx08w3DYN+xvhAsGt+MSyH0D6fp6ksSt043P5Vx2Hm4N89eVcZiXAphz5E+LMvAME4fByQsk837ToZg1fhmXApBHj5FJpN//qMmbiLbT+E41XiNUhiXQnh5dxd57goAxCyTtO3Q3jUQrFHjnHEnBMdRvHqgm9rEGIHQhjvPUKV4xp0Q2rsGSKZtYqN1CbhieXlXZ4BWjX/GnRC27u8umBSgLhHjpV2dBLGO8lZh3Alh7fbjBVdR4pZB31CajpPa0ge85RlXQsjYDpv2dFE31vgAd1JJKdi0uysgy8Y/Ra01CCFeAbKzNPuAHwH/ipvb52kp5TcrY96bef3QKRyliFmFM+aYJjzz6lE+ePlZAVg2/ikoBCFELWBIKZfk/K0NN5XLXuAxIcTFUsrNFbPSY+32Y6QzjpubrAD1iRhb959kMJmhvmbcrq0FRjHf0EKgXgjxtLf9N4AaKeUeACHEU7gBLhUVglKKlW0dBW8LWUzTnXnctKeLa86fVknTSibVcwpnOJn3s0TrJMx4PGCLihPCIPAd4H7gHNzIptw4hj5grn7T3syuI330DKZoLOHqdhzFE5vaIyUEJ53mhVtux0mOTOHofjbnzz/OvM/8WeB2FTNY3An8QkqpvExkp3CTQGYJJMBl1ZYOHFvlXV8YjYbaGC/u7KR/OF1By0pjYO9BnGSKWGPDaf/MRIKTGzaFYlcxQliKmyM5m0m8HhgQQswTQhi4sZEVDXCxHcVjG9upK/Feb5kGSinWvn6sQpaVTu/2nSgnf+C4VVtD3849KFtrLs2iKEYI/wFMFEKsBX6NK4w7gP/CDXrZnE1vWyk27e5kKJUhESv9adc0DJavP1gBq/xx8qVNo6bJNCwLlKJ/z/5AbYLiIp1SwG15Plqs35z8/Gb9AUa5iApSX2Ox60gvB070c/aUxsI7VBClFN2btmLV1Y6+jW1z6tXXaVowL0DLxsGE0vFTQ7y8q5PGOn+PgO7kkuK3G8LvFYY7jpLu7cOIj3EupknnupeCMyp72MCPWCK/23AQx3G7eL801MR49OV2BpOZwhtXkO6218DI71CTxaqrpXvT1lHHEZUi0kIYTtksX3+Q+pri5g5GI+uj8NTmDk2W+aPz+RehwEKYGYuhMhn6d+0LyCrvuIEerURWbukgmbaJ+xgkjiRhmfx8zW7skDyXlONwcsNGrPq6gts6GZuul4J9jIysEGxH8cDq3XkdVP1Qm7Do7k+F9ijZt2MXTjqDGSs81jHjMY6vej4Aq3KOGejRSmDt68c42Zcc2xOpREzD4CfP7ArFT6Fz3Us4meLGKFZdHX1yD+lTwXljR1IIjqO4f9Uu7dFK9TUWB08MsHFP8MvTR59cjVVTxGoZYJgGmAYnX3qlwlb9gUgKYePuTto7B6jX2BsAb4zW71+5M9BeYbD9CEMdRzFrixMCAEpx9MnVlTNqBJETglKK+1buwjAoaV2hWBprY8jDvbx6ILj8XyeefwFUaesksYZ6ul7cRGYwmPC9yAlhy/5udh/ppaFCPgRv9AqrdhbYUh8dDz+FUeLSsuE535x8MZinh8gJ4b6n3R+oEr1BlqbaGFv3dwfi8j7YfoTBA4fGnFYeDeU4dDzyVAWsOp1ICWH7oR62HeqhsbayHkWGYYCCB1bvquhxAI6tXINySrstZIk1NnBywybSvZUP6o2UEB5YvRtUZXuDLI11MTbs7GT/8ZHlIvWhlOLwQ49ilTJ
"text/plain": [
"<Figure size 122.4x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"plt.figure(figsize=figsize_violin)\n",
"# test = 'permutation_resampling'\n",
"test = 'mann_whitney'\n",
"\n",
"plt.title('Broad spiking')\n",
"violinplot(\n",
" results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(), \n",
" results.query('bs_stim==1 and frequency==11')['average_firing_rate'].to_numpy(), \n",
" test=test)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"rates_bs_11.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"rates_bs_11.png\", dpi=600, bbox_inches=\"tight\")\n",
"\n",
"plt.figure(figsize=figsize_violin)\n",
"plt.title('Narrow spiking')\n",
"violinplot(\n",
" results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(), \n",
" results.query('bs_stim==0 and frequency==11')['average_firing_rate'].to_numpy(), \n",
" test=test)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"rates_ns_11.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"rates_ns_11.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
analysis update
2019-12-13 10:43:57 +00:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 49606.0 p value 0.03757091416858637\n",
"U-test: U value 2333.0 p value 0.5891054506000802\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 122.4x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 122.4x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"source": [
"plt.figure(figsize=figsize_violin)\n",
"# test = 'permutation_resampling'\n",
"test = 'mann_whitney'\n",
"\n",
"plt.title('Broad spiking')\n",
"violinplot(\n",
" results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(), \n",
" results.query('bs_stim==1 and frequency==30')['average_firing_rate'].to_numpy(), \n",
" test=test)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"rates_bs.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"rates_bs.png\", dpi=600, bbox_inches=\"tight\")\n",
"\n",
"plt.figure(figsize=figsize_violin)\n",
"plt.title('Narrow spiking')\n",
"violinplot(\n",
" results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(), \n",
" results.query('bs_stim==0 and frequency==30')['average_firing_rate'].to_numpy(), \n",
" test=test)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"rates_ns_30.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"rates_ns_30.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"columns = [\n",
" 'average_firing_rate',\n",
" 'half_width',\n",
" 'peak_to_trough'\n",
"]\n",
"\n",
"\n",
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"bs = pd.DataFrame()\n",
"\n",
"bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)\n",
"bs['Stimulated'] = results.query('bs_stim==1')[columns].agg(summarize)\n",
"\n",
"ns = pd.DataFrame()\n",
"\n",
"ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)\n",
"ns['Stimulated'] = results.query('bs_stim==0')[columns].agg(summarize)\n",
"\n",
"\n",
"def MWU(column, df, cluster, extra):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
" df.query('bs_stim=={} {}'.format(cluster, extra))[column].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(column, df, cluster, extra):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
" df.query('bs_stim=={} {}'.format(cluster, extra))[column].dropna())\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"\n",
"bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))\n",
"bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))\n",
"\n",
"ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))\n",
"ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))\n",
"\n",
"bs.to_latex(output_path / \"statistics\" / \"broad_spiking.tex\")\n",
"bs.to_csv(output_path / \"statistics\" / \"broad_spiking.csv\")\n",
"\n",
"ns.to_latex(output_path / \"statistics\" / \"narrow_spiking.tex\")\n",
"ns.to_csv(output_path / \"statistics\" / \"narrow_spiking.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"columns = [\n",
" 'average_firing_rate',\n",
" 'half_width',\n",
" 'peak_to_trough'\n",
"]\n",
"\n",
"\n",
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"bs = pd.DataFrame()\n",
"\n",
"bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)\n",
"bs['Stimulated'] = results.query('bs_stim==1 and frequency==11')[columns].agg(summarize)\n",
"\n",
"ns = pd.DataFrame()\n",
"\n",
"ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)\n",
"ns['Stimulated'] = results.query('bs_stim==0 and frequency==11')[columns].agg(summarize)\n",
"\n",
"\n",
"def MWU(column, df, cluster, extra):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
" df.query('bs_stim=={} {} and frequency==11'.format(cluster, extra))[column].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(column, df, cluster, extra):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
" df.query('bs_stim=={} {} and frequency==11'.format(cluster, extra))[column].dropna())\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"\n",
"bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))\n",
"bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))\n",
"\n",
"ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))\n",
"ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))\n",
"\n",
"bs.to_latex(output_path / \"statistics\" / \"broad_spiking_11.tex\")\n",
"bs.to_csv(output_path / \"statistics\" / \"broad_spiking_11.csv\")\n",
"\n",
"ns.to_latex(output_path / \"statistics\" / \"narrow_spiking_11.tex\")\n",
"ns.to_csv(output_path / \"statistics\" / \"narrow_spiking_11.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"columns = [\n",
" 'average_firing_rate',\n",
" 'half_width',\n",
" 'peak_to_trough'\n",
"]\n",
"\n",
"\n",
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"bs = pd.DataFrame()\n",
"\n",
"bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)\n",
"bs['Stimulated'] = results.query('bs_stim==1 and frequency==30')[columns].agg(summarize)\n",
"\n",
"ns = pd.DataFrame()\n",
"\n",
"ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)\n",
"ns['Stimulated'] = results.query('bs_stim==0 and frequency==30')[columns].agg(summarize)\n",
"\n",
"\n",
"def MWU(column, df, cluster, extra):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
" df.query('bs_stim=={} {} and frequency==30'.format(cluster, extra))[column].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(column, df, cluster, extra):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(), \n",
" df.query('bs_stim=={} {} and frequency==30'.format(cluster, extra))[column].dropna())\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"\n",
"bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))\n",
"bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))\n",
"\n",
"ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))\n",
"ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))\n",
"\n",
"bs.to_latex(output_path / \"statistics\" / \"broad_spiking_30.tex\")\n",
"bs.to_csv(output_path / \"statistics\" / \"broad_spiking_30.csv\")\n",
"\n",
"ns.to_latex(output_path / \"statistics\" / \"narrow_spiking_30.tex\")\n",
"ns.to_csv(output_path / \"statistics\" / \"narrow_spiking_30.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# example waveforms"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def normalize(a):\n",
" t = a - a.min()\n",
" return t / t.max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'half_width','peak_to_trough'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=figsize_gen)\n",
"\n",
"\n",
"lw = 3\n",
"\n",
"row = results.query('bs==1').sort_values('half_width', ascending=False).iloc[50]\n",
"template = data_loader.template(\n",
" row.action, row.channel_group, row.unit_name)\n",
"\n",
"mean_wf = template.data\n",
"peak_wf = mean_wf[np.argmin(mean_wf.min(1))]\n",
"plt.plot(normalize(peak_wf.T), color=color_bs, lw=lw)\n",
"\n",
"\n",
"row = results.query('bs==0').sort_values('half_width').iloc[10]\n",
"template = data_loader.template(\n",
" row.action, row.channel_group, row.unit_name)\n",
"\n",
"mean_wf = template.data\n",
"peak_wf = mean_wf[np.argmin(mean_wf.min(1))]\n",
"plt.plot(normalize(peak_wf.T), color=color_ns, lw=lw)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"example_waveforms.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"example_waveforms.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 36,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"waveform-analysis\")"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 37,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"action.data['results'] = 'results.csv'\n",
"results.to_csv(action.data_path('results'), index=False)"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 38,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"stuff = {\n",
" \"figures\": \"figures\",\n",
" \"statistics\": \"statistics\"\n",
"}\n",
"\n",
"for key, value in stuff.items():\n",
" action.data[key] = value\n",
" data_path = action.data_path(key)\n",
" data_path.parent.mkdir(exist_ok=True, parents=True)\n",
" source = output_path / value\n",
" if source.is_file():\n",
" shutil.copy(source, data_path)\n",
" else:\n",
" copy_tree(str(source), str(data_path))"
]
},
{
"cell_type": "code",
analysis update
2019-12-13 10:43:57 +00:00
"execution_count": 39,
auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"10_waveform_analysis.ipynb\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}