actions/lfp_speed_stim/

actions/lfp_speed_stim/attributes.yaml

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+registered: '2020-08-28T10:17:36'
+data:
+  results: results.exdir
+  notebook: 20_lfp_speed.ipynb
+  html: 20_lfp_speed.html
+  figures: figures
+  statistics: statistics

actions/lfp_speed_stim/data/10_lfp_speed_stim.ipynb

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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": [
+      "09:52:58 [I] klustakwik KlustaKwik2 version 0.2.6\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
+      "  return f(*args, **kwds)\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
+      "  return f(*args, **kwds)\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pathlib\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import colors\n",
+    "import seaborn as sns\n",
+    "import re\n",
+    "import shutil\n",
+    "import pandas as pd\n",
+    "import scipy.stats\n",
+    "\n",
+    "import exdir\n",
+    "import expipe\n",
+    "from distutils.dir_util import copy_tree\n",
+    "import septum_mec\n",
+    "import spatial_maps as sp\n",
+    "import head_direction.head as head\n",
+    "import septum_mec.analysis.data_processing as dp\n",
+    "import septum_mec.analysis.registration\n",
+    "from septum_mec.analysis.plotting import violinplot, despine\n",
+    "from spatial_maps.fields import (\n",
+    "    find_peaks, calculate_field_centers, separate_fields_by_laplace, \n",
+    "    map_pass_to_unit_circle, calculate_field_centers, distance_to_edge_function, \n",
+    "    which_field, compute_crossings)\n",
+    "from phase_precession import cl_corr\n",
+    "from spike_statistics.core import permutation_resampling\n",
+    "import matplotlib.mlab as mlab\n",
+    "import scipy.signal as ss\n",
+    "from scipy.interpolate import interp1d\n",
+    "from septum_mec.analysis.plotting import regplot\n",
+    "from skimage import measure\n",
+    "from tqdm.notebook import tqdm_notebook as tqdm\n",
+    "tqdm.pandas()\n",
+    "\n",
+    "import pycwt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_speed = 1 # m/s only used for speed score\n",
+    "min_speed = 0.02 # m/s only used for speed score\n",
+    "position_sampling_rate = 1000 # for interpolation\n",
+    "position_low_pass_frequency = 6 # for low pass filtering of position\n",
+    "\n",
+    "box_size = [1.0, 1.0]\n",
+    "bin_size = 0.02\n",
+    "\n",
+    "speed_binsize = 0.02\n",
+    "\n",
+    "stim_mask = True\n",
+    "baseline_duration = 600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_loader = dp.Data(\n",
+    "    position_sampling_rate=position_sampling_rate, \n",
+    "    position_low_pass_frequency=position_low_pass_frequency,\n",
+    "    box_size=box_size, bin_size=bin_size, \n",
+    "    stim_mask=stim_mask, baseline_duration=baseline_duration\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_path = dp.project_path()\n",
+    "project = expipe.get_project(project_path)\n",
+    "actions = project.actions\n",
+    "\n",
+    "output_path = pathlib.Path(\"output\") / \"lfp-speed-stim\"\n",
+    "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
+    "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "identify_neurons = actions['identify-neurons']\n",
+    "sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "channel_groups = []\n",
+    "for i, row in sessions.iterrows():\n",
+    "    for ch in range(8):\n",
+    "        row['channel_group'] = ch\n",
+    "        channel_groups.append(row.to_dict())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sessions = pd.DataFrame(channel_groups)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def signaltonoise(a, axis=0, ddof=0):\n",
+    "    a = np.asanyarray(a)\n",
+    "    m = a.mean(axis)\n",
+    "    sd = a.std(axis=axis, ddof=ddof)\n",
+    "    return np.where(sd == 0, 0, m / sd)\n",
+    "\n",
+    "\n",
+    "def remove_artifacts(anas, spikes=None, width=500, threshold=2, sampling_rate=None, fillval=0):\n",
+    "    sampling_rate = sampling_rate or anas.sampling_rate.magnitude\n",
+    "    times = np.arange(anas.shape[0]) / sampling_rate\n",
+    "    anas = np.array(anas)\n",
+    "    if anas.ndim == 1:\n",
+    "        anas = np.reshape(anas, (anas.size, 1))\n",
+    "    assert len(times) == anas.shape[0]\n",
+    "    nchan = anas.shape[1]\n",
+    "    if spikes is not None:\n",
+    "        spikes = np.array(spikes)\n",
+    "    for ch in range(nchan):\n",
+    "        idxs, = np.where(abs(anas[:, ch]) > threshold)\n",
+    "        for idx in idxs:\n",
+    "            if spikes is not None:\n",
+    "                t0 = times[idx-width]\n",
+    "                stop = idx+width\n",
+    "                if stop > len(times) - 1:\n",
+    "                    stop = len(times) - 1 \n",
+    "                t1 = times[stop]\n",
+    "                mask = (spikes > t0) & (spikes < t1)\n",
+    "                spikes = spikes[~mask]\n",
+    "            anas[idx-width:idx+width, ch] = fillval\n",
+    "    if spikes is not None:\n",
+    "        spikes = spikes[spikes <= times[-1]]\n",
+    "        return anas, times, spikes\n",
+    "    else:\n",
+    "        return anas, times\n",
+    "    \n",
+    "def find_theta_peak(p, f, f1, f2):\n",
+    "    if np.all(np.isnan(p)):\n",
+    "        return np.nan, np.nan\n",
+    "    mask = (f > f1) & (f < f2)\n",
+    "    p_m = p[mask]\n",
+    "    f_m = f[mask]\n",
+    "    peaks = find_peaks(p_m)\n",
+    "    idx = np.argmax(p_m[peaks])\n",
+    "    return f_m[peaks[idx]], p_m[peaks[idx]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def zscore(a):\n",
+    "    return (a - a.mean()) / a.std()\n",
+    "#     return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_stim_freq(action_id):\n",
+    "    stim_times = data_loader.stim_times(action_id)\n",
+    "    if stim_times is None:\n",
+    "        return\n",
+    "    stim_times = np.array(stim_times)\n",
+    "    return 1 / np.mean(np.diff(stim_times))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = exdir.File(output_path / 'results')\n",
+    "\n",
+    "mother = pycwt.Morlet(80)\n",
+    "NFFT = 2056\n",
+    "\n",
+    "def process(row):\n",
+    "    name = row['action'] + '-' + str(row['channel_group'])\n",
+    "    stim_freq = compute_stim_freq(row['action'])\n",
+    "    if stim_freq is None:\n",
+    "        return\n",
+    "    \n",
+    "    flim = [stim_freq - 2, stim_freq + 2]\n",
+    "    \n",
+    "    lfp = data_loader.lfp(row.action, row.channel_group)\n",
+    "    sample_rate = lfp.sampling_rate.magnitude\n",
+    "    sampling_period = 1 / sample_rate\n",
+    "    x, y, t, speed = map(data_loader.tracking(row.action).get, ['x', 'y', 't', 'v'])\n",
+    "    cleaned_lfp, times = remove_artifacts(lfp)\n",
+    "    speed = interp1d(t, speed, bounds_error=False, fill_value='extrapolate')(times)\n",
+    "    peak_amp = {}\n",
+    "    for i, ch in enumerate(cleaned_lfp.T):\n",
+    "        pxx, freqs = mlab.psd(ch, Fs=lfp.sampling_rate.magnitude, NFFT=4000)\n",
+    "        f, p = find_theta_peak(pxx, freqs, 6, 10)\n",
+    "        peak_amp[i] = p\n",
+    "\n",
+    "    theta_channel = max(peak_amp, key=lambda x: peak_amp[x])\n",
+    "    signal = zscore(cleaned_lfp[:,theta_channel])\n",
+    "    \n",
+    "    if name in output:\n",
+    "        return\n",
+    "    \n",
+    "    \n",
+    "    results = output.require_group(name)\n",
+    "    freqs = np.arange(*flim, .1)\n",
+    "    wave, scales, freqs, coi, fft, fftfreqs = pycwt.cwt(\n",
+    "        signal, sampling_period, freqs=freqs, wavelet=mother)\n",
+    "    \n",
+    "    power = (np.abs(wave)) ** 2\n",
+    "    power /= scales[:, None] #rectify the power spectrum according to the suggestions proposed by Liu et al. (2007)\n",
+    "    \n",
+    "    theta_freq = np.array([freqs[i] for i in np.argmax(power, axis=0)])\n",
+    "    theta_power = np.mean(power, axis=0)\n",
+    "\n",
+    "    speed_bins = np.arange(min_speed, max_speed + speed_binsize, speed_binsize)\n",
+    "    ia = np.digitize(speed, bins=speed_bins, right=True)\n",
+    "    mean_freq = np.zeros_like(speed_bins)\n",
+    "    mean_power = np.zeros_like(speed_bins)\n",
+    "    for i in range(len(speed_bins)):\n",
+    "        mean_freq[i] = np.mean(theta_freq[ia==i])\n",
+    "        mean_power[i] = np.mean(theta_power[ia==i])\n",
+    "        \n",
+    "    freq_score = np.corrcoef(speed, theta_freq)[0,1]\n",
+    "    power_score = np.corrcoef(speed, theta_power)[0,1]\n",
+    "    \n",
+    "    results.attrs = {\n",
+    "        'freq_score': float(freq_score),\n",
+    "        'sample_rate': float(sample_rate),\n",
+    "        'power_score': float(power_score),\n",
+    "        'action': row['action'],\n",
+    "        'channel_group': int(row['channel_group']),\n",
+    "        'max_speed': max_speed,\n",
+    "        'min_speed': min_speed,\n",
+    "        'position_low_pass_frequency': position_low_pass_frequency\n",
+    "    }\n",
+    "    \n",
+    "    results.create_dataset('wavelet_power', data=power)\n",
+    "    results.create_dataset('wavelet_freqs', data=freqs)\n",
+    "    results.create_dataset('theta_freq', data=theta_freq)\n",
+    "    results.create_dataset('theta_power', data=theta_power)\n",
+    "    results.create_dataset('speed', data=speed)\n",
+    "    results.create_dataset('mean_freq', data=mean_freq)\n",
+    "    results.create_dataset('mean_power', data=mean_power)\n",
+    "    results.create_dataset('speed_bins', data=speed_bins)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f5b845ccb4c460780c75e4829109535",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=696), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
+      "  out=out, **kwargs)\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "sessions.progress_apply(process, axis=1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Store results in Expipe action"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "action = project.require_action(\"lfp_speed_stim\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "action.data[\"results\"] = \"results.exdir\"\n",
+    "copy_tree(output_path, str(action.data_path()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "septum_mec.analysis.registration.store_notebook(action, \"10_lfp_speed_stim.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": 4
+}

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/attributes.yaml

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+freq_score: -0.006285614855385158
+sample_rate: 1000.0
+power_score: -0.01578935674061475
+action: 1833-010719-2
+channel_group: 0
+max_speed: 1
+min_speed: 0.02
+position_low_pass_frequency: 6

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/exdir.yaml

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+exdir:
+   type: "group"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/mean_freq/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/mean_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/speed/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/speed_bins/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/theta_freq/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/theta_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/wavelet_freqs/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-0/wavelet_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/attributes.yaml

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+freq_score: 0.01429389029449852
+sample_rate: 1000.0
+power_score: -0.04961110310019039
+action: 1833-010719-2
+channel_group: 1
+max_speed: 1
+min_speed: 0.02
+position_low_pass_frequency: 6

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/exdir.yaml

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+exdir:
+   type: "group"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/mean_freq/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/mean_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/speed/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/speed_bins/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/theta_freq/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/theta_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/wavelet_freqs/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-1/wavelet_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/attributes.yaml

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+freq_score: -0.15166158381576225
+sample_rate: 1000.0
+power_score: -0.015842922378173682
+action: 1833-010719-2
+channel_group: 2
+max_speed: 1
+min_speed: 0.02
+position_low_pass_frequency: 6

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/exdir.yaml

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+exdir:
+   type: "group"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/mean_freq/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/mean_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/speed/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/speed_bins/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/theta_freq/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/theta_power/exdir.yaml

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+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/wavelet_freqs/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-2/wavelet_power/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/attributes.yaml

@@ -0,0 +1,8 @@
+freq_score: 0.5003745629460933
+sample_rate: 1000.0
+power_score: -0.44626290964950766
+action: 1833-010719-2
+channel_group: 3
+max_speed: 1
+min_speed: 0.02
+position_low_pass_frequency: 6

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "group"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/mean_freq/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/mean_power/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/speed/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/speed_bins/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/theta_freq/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/theta_power/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/wavelet_freqs/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-3/wavelet_power/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/attributes.yaml

@@ -0,0 +1,8 @@
+freq_score: -0.062264821287393926
+sample_rate: 1000.0
+power_score: 0.04839495976379853
+action: 1833-010719-2
+channel_group: 4
+max_speed: 1
+min_speed: 0.02
+position_low_pass_frequency: 6

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "group"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/mean_freq/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/mean_power/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/speed/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/speed_bins/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/theta_freq/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1

actions/lfp_speed_stim/data/results.exdir/1833-010719-2-4/theta_power/exdir.yaml

@@ -0,0 +1,3 @@
+exdir:
+   type: "dataset"
+   version: 1