Mikkel
/
septum-mec
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
septum-mec/actions/waveform-analysis/data/10_waveform_analysis.ipynb

1190 lines
38 KiB
Plaintext
Raw Normal View History

auto commit actions/waveform-analysis
2019-10-09 12:24:44 +00:00
{
"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": [
"14:00:33 [I] klustakwik KlustaKwik2 version 0.2.6\n"
]
}
],
"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",
"from tqdm import tqdm_notebook as tqdm\n",
"from tqdm._tqdm_notebook import tqdm_notebook\n",
"tqdm_notebook.pandas()\n",
"\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",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N cells: 1298\n"
]
}
],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
"units = pd.read_csv(identify_neurons.data_path('all_non_identified_units'))\n",
"# units = pd.read_csv(identify_neurons.data_path('units'))\n",
"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",
"execution_count": null,
"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",
" <th>channel_group</th>\n",
" <th>unit_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</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",
" <td>1</td>\n",
" <td>104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</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",
" <td>1</td>\n",
" <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",
" <td>1</td>\n",
" <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",
" <td>1</td>\n",
" <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",
" <td>1</td>\n",
" <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",
" stim_location stimulated tag channel_group unit_name \n",
"0 NaN False baseline ii 1 104 \n",
"1 NaN False baseline ii 1 108 \n",
"2 NaN False baseline ii 1 85 \n",
"3 NaN False baseline ii 1 94 \n",
"4 NaN False baseline ii 1 98 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"session_units.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"action\n",
"1849-110319-2 13\n",
"1849-010319-4 13\n",
"1849-220319-4 13\n",
"1834-220319-3 13\n",
"1833-200619-1 13\n",
"1849-220319-3 13\n",
"1849-060319-3 13\n",
"1834-110319-1 13\n",
"1833-050619-3 13\n",
"1833-120619-3 13\n",
"1833-050619-4 13\n",
"1833-060619-1 13\n",
"1834-120319-3 12\n",
"1834-120319-2 12\n",
"1839-120619-3 12\n",
"1849-220319-5 12\n",
"1834-150319-1 12\n",
"1839-290519-1 12\n",
"1834-010319-5 11\n",
"1849-060319-4 11\n",
"1849-060319-2 11\n",
"1834-120319-4 10\n",
"1839-120619-2 10\n",
"1839-060619-5 9\n",
"1833-050619-1 9\n",
"1834-110319-5 9\n",
"1834-110319-3 9\n",
"1833-120619-4 9\n",
"1834-120319-1 8\n",
"1833-120619-1 7\n",
"1839-290519-3 6\n",
"1839-200619-1 5\n",
"1834-110319-6 5\n",
"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",
"Name: unit_name, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"session_units.groupby('action').count().unit_name.hist()\n",
"# session_units.groupby('action').count().sort_values('unit_name', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Process all data"
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fb8ac000ef5b473a833b96cbfd854d1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": {},
"outputs": [],
"source": [
"%debug"
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"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",
"execution_count": null,
"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",
"execution_count": null,
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results.average_firing_rate = results.apply(lambda x: float(x.average_firing_rate), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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": {},
"outputs": [],
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"waveform-analysis\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"action.data['results'] = 'results.csv'\n",
"results.to_csv(action.data_path('results'), index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"execution_count": null,
"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
}