diff --git a/actions/stimulus-lfp-response/attributes.yaml b/actions/stimulus-lfp-response/attributes.yaml new file mode 100644 index 000000000..3a47276e0 --- /dev/null +++ b/actions/stimulus-lfp-response/attributes.yaml @@ -0,0 +1,5 @@ +registered: '2019-10-14T17:03:05' +data: + results: results.csv + notebook: 10-calculate-stimulus-lfp-response.ipynb + html: 10-calculate-stimulus-lfp-response.html diff --git a/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html new file mode 100644 index 000000000..97b6938f9 --- /dev/null +++ b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.html @@ -0,0 +1,13628 @@ + + + + +10-calculate-stimulus-lfp-response + + + + + + + + + + + + + + + + + + + + + + + +
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In [1]:
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+
%load_ext autoreload
+%autoreload 2
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+ +
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+ +
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In [2]:
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import matplotlib.pyplot as plt
+%matplotlib inline
+import spatial_maps as sp
+import septum_mec.analysis.data_processing as dp
+import septum_mec.analysis.registration
+import expipe
+import os
+import pathlib
+import numpy as np
+import exdir
+import pandas as pd
+import optogenetics as og
+import quantities as pq
+import shutil
+from distutils.dir_util import copy_tree
+
+from scipy.signal import find_peaks
+from scipy.interpolate import interp1d
+from matplotlib import mlab
+
+from tqdm import tqdm_notebook as tqdm
+from tqdm._tqdm_notebook import tqdm_notebook
+tqdm_notebook.pandas()
+
+ +
+
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+ +
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+ + +
+ +
+ + +
+
15:15:05 [I] klustakwik KlustaKwik2 version 0.2.6
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In [3]:
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data_loader = dp.Data()
+actions = data_loader.actions
+project = data_loader.project
+
+ +
+
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+ +
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+
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+
output = pathlib.Path('output/stimulus-lfp-response')
+(output / 'figures').mkdir(parents=True, exist_ok=True)
+
+ +
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+ +
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+
In [5]:
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identify_neurons = actions['identify-neurons']
+sessions = pd.read_csv(identify_neurons.data_path('sessions'))
+
+ +
+
+
+ +
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+
In [6]:
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+
+
channel_groups = []
+for i, row in sessions.iterrows():
+    for ch in range(8):
+        row['channel_group'] = ch
+        channel_groups.append(row.to_dict())
+
+ +
+
+
+ +
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+
In [7]:
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+
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channel_groups = pd.DataFrame(channel_groups)
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+ +
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+ +
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+
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+
+
def get_lim(action_id):
+    stim_times = data_loader.stim_times(action_id)
+    if stim_times is None:
+        return [0, np.inf]
+    stim_times = np.array(stim_times)
+    return [stim_times.min(), stim_times.max()]
+
+def get_mask(lfp, lim):
+    return (lfp.times >= lim[0]) & (lfp.times <= lim[1])
+
+def zscore(a):
+    return (a - a.mean()) / a.std()
+
+def compute_stim_freq(action_id):
+    stim_times = data_loader.stim_times(action_id)
+    if stim_times is None:
+        return np.nan
+    stim_times = np.array(stim_times)
+    return 1 / np.mean(np.diff(stim_times))
+
+ +
+
+
+ +
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+
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+
+
+
def signaltonoise(a, axis=0, ddof=0):
+    a = np.asanyarray(a)
+    m = a.mean(axis)
+    sd = a.std(axis=axis, ddof=ddof)
+    return np.where(sd == 0, 0, m / sd)
+
+
+def select_and_clean(anas, width=500, threshold=2):
+    anas = np.array(anas)
+
+    for ch in range(anas.shape[1]):
+        idxs, = np.where(abs(anas[:, ch]) > threshold)
+        for idx in idxs:
+            anas[idx-width:idx+width, ch] = 0 # TODO AR model prediction
+    return anas
+
+ +
+
+
+ +
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+
+
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+
def compute_energy(p, f, f1, f2):
+    if np.isnan(f1):
+        return np.nan
+    mask = (f > f1) & (f < f2)
+    df = f[1] - f[0]
+    return np.sum(p[mask]) * df
+
+ +
+
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+ +
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+
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+
+
def find_theta_peak(p, f):
+    mask = (f > 6) & (f < 10)
+    p_m = p[mask]
+    f_m = f[mask]
+    peaks, _ = find_peaks(p_m)
+    idx = np.argmax(p_m[peaks])
+    return f_m[peaks[idx]], p_m[peaks[idx]]
+
+ +
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+ +
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def compute_half_width(p, f, m_p, m_f):
+    if np.isnan(m_p):
+        return np.nan, np.nan
+    m_p_half = m_p / 2
+    half_p = p - m_p_half
+    idx_f = np.where(f <= m_f)[0].max()
+    idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)
+    if len(idxs_p1) == 0:
+        return np.nan, np.nan
+    m1 = idxs_p1.max()
+    idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)
+    m2 = idxs_p2.min() + idx_f    
+#     plt.plot(f, p)
+#     plt.plot(m_f, m_p, marker='o', ls='none', markersize=10)
+#     plt.plot(f[m1], p[m1], marker='x', ls='none', markersize=10, c='r')
+#     plt.plot(f[m1+1], p[m1+1], marker='+', ls='none', markersize=10, c='r')
+    
+#     plt.plot(f[m2], p[m2], marker='x', ls='none', markersize=10, c='k')
+#     plt.plot(f[m2+1], p[m2+1], marker='+', ls='none', markersize=10, c='k')
+    
+#     plt.xlim(4,12)
+    assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])
+    assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])
+    
+    f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)
+    f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)
+    return f1, f2
+
+ +
+
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+ +
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+
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+
+
def compute_stim_peak(p, f, s_f):
+    if np.isnan(s_f):
+        return np.nan
+    return interp1d(f, p)(s_f)
+
+ +
+
+
+ +
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+
+
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+
NFFT = 10000
+theta_band_f1, theta_band_f2 = 6, 10 
+
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+
+
def process(row):
+    action_id = row['action']
+    channel_group = row['channel_group']
+    lfp = data_loader.lfp(action_id, channel_group)
+    clean_lfp = select_and_clean(lfp)
+    snls = signaltonoise(clean_lfp)
+    best_channel = np.argmax(snls)
+    snl = snls[best_channel]
+    
+    lim = get_lim(action_id)
+    
+    mask = get_mask(lfp, lim)
+    signal = zscore(clean_lfp[mask, best_channel].ravel())
+    
+    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)
+    
+    theta_f, theta_p_max = find_theta_peak(p_xx, freq)
+    
+    theta_energy = compute_energy(p_xx, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz
+    
+    theta_half_f1, theta_half_f2 = compute_half_width(p_xx, freq, theta_p_max, theta_f)
+    
+    theta_half_width = theta_half_f2 - theta_half_f1
+    
+    theta_half_energy = compute_energy(p_xx, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz
+    
+    # stim
+    
+    stim_freq = compute_stim_freq(action_id)
+    
+    stim_p_max = compute_stim_peak(p_xx, freq, stim_freq)
+    
+    stim_half_f1, stim_half_f2 = compute_half_width(p_xx, freq, stim_p_max, stim_freq)
+    stim_half_width = stim_half_f2 - stim_half_f1
+    
+    stim_energy = compute_energy(p_xx, freq, stim_half_f1, stim_half_f2)
+    
+    result = pd.Series({
+        'signal_to_noise': snl,
+        'best_channel': best_channel,
+        'theta_freq': theta_f,
+        'theta_peak': theta_p_max,
+        'theta_energy': theta_energy,
+        'theta_half_f1': theta_half_f1, 
+        'theta_half_f2': theta_half_f2,
+        'theta_half_width': theta_half_width,
+        'theta_half_energy': theta_half_energy,
+        'stim_freq': stim_freq,
+        'stim_p_max': stim_p_max,
+        'stim_half_f1': stim_half_f1, 
+        'stim_half_f2': stim_half_f2,
+        'stim_half_width': stim_half_width,
+        'stim_energy': stim_energy
+    })
+    return result
+
+ +
+
+
+ +
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+
+
In [ ]:
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+
results = channel_groups.merge(
+    channel_groups.progress_apply(process, axis=1), 
+    left_index=True, right_index=True)
+
+ +
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+ +
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+ + +
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+

Save to expipe

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action = project.require_action("stimulus-lfp-response")
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+ +
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+ +
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+
action.modules['parameters'] = {
+    'NFFT': NFFT,
+    'theta_band_f1': theta_band_f1,
+    'theta_band_f2': theta_band_f2
+}
+
+ +
+
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+ +
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action.data['results'] = 'results.csv'
+results.to_csv(action.data_path('results'), index=False)
+
+ +
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+ +
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copy_tree(output, str(action.data_path()))
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+ +
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septum_mec.analysis.registration.store_notebook(action, "10-calculate-stimulus-lfp-response.ipynb")
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+ +
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+ +
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+
In [ ]:
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+
 
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+ + + + + + diff --git a/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb new file mode 100644 index 000000000..e3f40b21f --- /dev/null +++ b/actions/stimulus-lfp-response/data/10-calculate-stimulus-lfp-response.ipynb @@ -0,0 +1,416 @@ +{ + "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": [ + "15:15:05 [I] klustakwik KlustaKwik2 version 0.2.6\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import spatial_maps as sp\n", + "import septum_mec.analysis.data_processing as dp\n", + "import septum_mec.analysis.registration\n", + "import expipe\n", + "import os\n", + "import pathlib\n", + "import numpy as np\n", + "import exdir\n", + "import pandas as pd\n", + "import optogenetics as og\n", + "import quantities as pq\n", + "import shutil\n", + "from distutils.dir_util import copy_tree\n", + "\n", + "from scipy.signal import find_peaks\n", + "from scipy.interpolate import interp1d\n", + "from matplotlib import mlab\n", + "\n", + "from tqdm import tqdm_notebook as tqdm\n", + "from tqdm._tqdm_notebook import tqdm_notebook\n", + "tqdm_notebook.pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_loader = dp.Data()\n", + "actions = data_loader.actions\n", + "project = data_loader.project" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "output = pathlib.Path('output/stimulus-lfp-response')\n", + "(output / 'figures').mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "identify_neurons = actions['identify-neurons']\n", + "sessions = pd.read_csv(identify_neurons.data_path('sessions'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "channel_groups = pd.DataFrame(channel_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_lim(action_id):\n", + " stim_times = data_loader.stim_times(action_id)\n", + " if stim_times is None:\n", + " return [0, np.inf]\n", + " stim_times = np.array(stim_times)\n", + " return [stim_times.min(), stim_times.max()]\n", + "\n", + "def get_mask(lfp, lim):\n", + " return (lfp.times >= lim[0]) & (lfp.times <= lim[1])\n", + "\n", + "def zscore(a):\n", + " return (a - a.mean()) / a.std()\n", + "\n", + "def compute_stim_freq(action_id):\n", + " stim_times = data_loader.stim_times(action_id)\n", + " if stim_times is None:\n", + " return np.nan\n", + " stim_times = np.array(stim_times)\n", + " return 1 / np.mean(np.diff(stim_times))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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 select_and_clean(anas, width=500, threshold=2):\n", + " anas = np.array(anas)\n", + "\n", + " for ch in range(anas.shape[1]):\n", + " idxs, = np.where(abs(anas[:, ch]) > threshold)\n", + " for idx in idxs:\n", + " anas[idx-width:idx+width, ch] = 0 # TODO AR model prediction\n", + " return anas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_energy(p, f, f1, f2):\n", + " if np.isnan(f1):\n", + " return np.nan\n", + " mask = (f > f1) & (f < f2)\n", + " df = f[1] - f[0]\n", + " return np.sum(p[mask]) * df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def find_theta_peak(p, f):\n", + " mask = (f > 6) & (f < 10)\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": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_half_width(p, f, m_p, m_f):\n", + " if np.isnan(m_p):\n", + " return np.nan, np.nan\n", + " m_p_half = m_p / 2\n", + " half_p = p - m_p_half\n", + " idx_f = np.where(f <= m_f)[0].max()\n", + " idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)\n", + " if len(idxs_p1) == 0:\n", + " return np.nan, np.nan\n", + " m1 = idxs_p1.max()\n", + " idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)\n", + " m2 = idxs_p2.min() + idx_f \n", + "# plt.plot(f, p)\n", + "# plt.plot(m_f, m_p, marker='o', ls='none', markersize=10)\n", + "# plt.plot(f[m1], p[m1], marker='x', ls='none', markersize=10, c='r')\n", + "# plt.plot(f[m1+1], p[m1+1], marker='+', ls='none', markersize=10, c='r')\n", + " \n", + "# plt.plot(f[m2], p[m2], marker='x', ls='none', markersize=10, c='k')\n", + "# plt.plot(f[m2+1], p[m2+1], marker='+', ls='none', markersize=10, c='k')\n", + " \n", + "# plt.xlim(4,12)\n", + " assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n", + " assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n", + " \n", + " f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)\n", + " f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)\n", + " return f1, f2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_stim_peak(p, f, s_f):\n", + " if np.isnan(s_f):\n", + " return np.nan\n", + " return interp1d(f, p)(s_f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NFFT = 10000\n", + "theta_band_f1, theta_band_f2 = 6, 10 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def process(row):\n", + " action_id = row['action']\n", + " channel_group = row['channel_group']\n", + " lfp = data_loader.lfp(action_id, channel_group)\n", + " clean_lfp = select_and_clean(lfp)\n", + " snls = signaltonoise(clean_lfp)\n", + " best_channel = np.argmax(snls)\n", + " snl = snls[best_channel]\n", + " \n", + " lim = get_lim(action_id)\n", + " \n", + " mask = get_mask(lfp, lim)\n", + " signal = zscore(clean_lfp[mask, best_channel].ravel())\n", + " \n", + " p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n", + " \n", + " theta_f, theta_p_max = find_theta_peak(p_xx, freq)\n", + " \n", + " theta_energy = compute_energy(p_xx, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz\n", + " \n", + " theta_half_f1, theta_half_f2 = compute_half_width(p_xx, freq, theta_p_max, theta_f)\n", + " \n", + " theta_half_width = theta_half_f2 - theta_half_f1\n", + " \n", + " theta_half_energy = compute_energy(p_xx, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz\n", + " \n", + " # stim\n", + " \n", + " stim_freq = compute_stim_freq(action_id)\n", + " \n", + " stim_p_max = compute_stim_peak(p_xx, freq, stim_freq)\n", + " \n", + " stim_half_f1, stim_half_f2 = compute_half_width(p_xx, freq, stim_p_max, stim_freq)\n", + " stim_half_width = stim_half_f2 - stim_half_f1\n", + " \n", + " stim_energy = compute_energy(p_xx, freq, stim_half_f1, stim_half_f2)\n", + " \n", + " result = pd.Series({\n", + " 'signal_to_noise': snl,\n", + " 'best_channel': best_channel,\n", + " 'theta_freq': theta_f,\n", + " 'theta_peak': theta_p_max,\n", + " 'theta_energy': theta_energy,\n", + " 'theta_half_f1': theta_half_f1, \n", + " 'theta_half_f2': theta_half_f2,\n", + " 'theta_half_width': theta_half_width,\n", + " 'theta_half_energy': theta_half_energy,\n", + " 'stim_freq': stim_freq,\n", + " 'stim_p_max': stim_p_max,\n", + " 'stim_half_f1': stim_half_f1, \n", + " 'stim_half_f2': stim_half_f2,\n", + " 'stim_half_width': stim_half_width,\n", + " 'stim_energy': stim_energy\n", + " })\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "81d917d31c0e43cf80ec3398d46b2c01", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=704), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = channel_groups.merge(\n", + " channel_groups.progress_apply(process, axis=1), \n", + " left_index=True, right_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save to expipe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "action = project.require_action(\"stimulus-lfp-response\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "action.modules['parameters'] = {\n", + " 'NFFT': NFFT,\n", + " 'theta_band_f1': theta_band_f1,\n", + " 'theta_band_f2': theta_band_f2\n", + "}" + ] + }, + { + "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": [ + "copy_tree(output, str(action.data_path()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-lfp-response.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": 2 +} diff --git a/actions/stimulus-lfp-response/data/results.csv b/actions/stimulus-lfp-response/data/results.csv new file mode 100644 index 000000000..684ea7ca2 --- /dev/null +++ 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b/actions/stimulus-lfp-response/modules/parameters.yaml new file mode 100644 index 000000000..17593dd5d --- /dev/null +++ b/actions/stimulus-lfp-response/modules/parameters.yaml @@ -0,0 +1,3 @@ +NFFT: 10000 +theta_band_f1: 6 +theta_band_f2: 10