Fig1C-F

Analysis/Dependencies/functions.py

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+from numba import jit
+import numpy as np
+import pandas as pd
+from Dependencies import neuroseries as nts 
+import sys, os
+import scipy
+from scipy import signal
+from itertools import combinations
+
+'''
+Utilities functions
+Feel free to add your own
+'''
+
+#########################################################
+# CORRELATION
+#########################################################
+@jit(nopython=True)
+def crossCorr(t1, t2, binsize, nbins):
+    ''' 
+        Fast crossCorr 
+    '''
+    nt1 = len(t1)
+    nt2 = len(t2)
+    if np.floor(nbins/2)*2 == nbins:
+        nbins = nbins+1
+
+    m = -binsize*((nbins+1)/2)
+    B = np.zeros(nbins)
+    for j in range(nbins):
+        B[j] = m+j*binsize
+
+    w = ((nbins/2) * binsize)
+    C = np.zeros(nbins)
+    i2 = 1
+
+    for i1 in range(nt1):
+        lbound = t1[i1] - w
+        while i2 < nt2 and t2[i2] < lbound:
+            i2 = i2+1
+        while i2 > 1 and t2[i2-1] > lbound:
+            i2 = i2-1
+
+        rbound = lbound
+        l = i2
+        for j in range(nbins):
+            k = 0
+            rbound = rbound+binsize
+            while l < nt2 and t2[l] < rbound:
+                l = l+1
+                k = k+1
+
+            C[j] += k
+
+    # for j in range(nbins):
+    # C[j] = C[j] / (nt1 * binsize)
+    C = C/(nt1 * binsize/1000)
+
+    return C
+
+def refineSleepFromAccel(acceleration, sleep_ep):
+    vl = acceleration[0].restrict(sleep_ep)
+    vl = vl.as_series().diff().abs().dropna()    
+    a, _ = scipy.signal.find_peaks(vl, 0.025)
+    peaks = nts.Tsd(vl.iloc[a])
+    duration = np.diff(peaks.as_units('s').index.values)
+    interval = nts.IntervalSet(start = peaks.index.values[0:-1], end = peaks.index.values[1:])
+
+    newsleep_ep = interval.iloc[duration>15.0]
+    newsleep_ep = newsleep_ep.reset_index(drop=True)
+    newsleep_ep = newsleep_ep.merge_close_intervals(100000, time_units ='us')
+
+    newsleep_ep    = sleep_ep.intersect(newsleep_ep)
+
+    return newsleep_ep
+
+#########################################################
+# LFP FUNCTIONS
+#########################################################
+def butter_bandpass(lowcut, highcut, fs, order=5):
+    from scipy.signal import butter
+    nyq = 0.5 * fs
+    low = lowcut / nyq
+    high = highcut / nyq
+    b, a = butter(order, [low, high], btype='band')
+    return b, a
+
+def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
+    from scipy.signal import filtfilt
+    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
+    y = filtfilt(b, a, data)
+    return y
+
+
+

Analysis/Dependencies/neuroseries/__init__.py

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+from .interval_set import *
+from .time_series import *