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 @@ + + +
+ +%load_ext autoreload
+%autoreload 2
+
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()
+
data_loader = dp.Data()
+actions = data_loader.actions
+project = data_loader.project
+
output = pathlib.Path('output/stimulus-lfp-response')
+(output / 'figures').mkdir(parents=True, exist_ok=True)
+
identify_neurons = actions['identify-neurons']
+sessions = pd.read_csv(identify_neurons.data_path('sessions'))
+
channel_groups = []
+for i, row in sessions.iterrows():
+ for ch in range(8):
+ row['channel_group'] = ch
+ channel_groups.append(row.to_dict())
+
channel_groups = pd.DataFrame(channel_groups)
+
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))
+
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
+
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
+
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]]
+
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
+
def compute_stim_peak(p, f, s_f):
+ if np.isnan(s_f):
+ return np.nan
+ return interp1d(f, p)(s_f)
+
NFFT = 10000
+theta_band_f1, theta_band_f2 = 6, 10
+
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
+
results = channel_groups.merge(
+ channel_groups.progress_apply(process, axis=1),
+ left_index=True, right_index=True)
+
action = project.require_action("stimulus-lfp-response")
+
action.modules['parameters'] = {
+ 'NFFT': NFFT,
+ 'theta_band_f1': theta_band_f1,
+ 'theta_band_f2': theta_band_f2
+}
+
action.data['results'] = 'results.csv'
+results.to_csv(action.data_path('results'), index=False)
+
copy_tree(output, str(action.data_path()))
+
septum_mec.analysis.registration.store_notebook(action, "10-calculate-stimulus-lfp-response.ipynb")
+
+